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Alignment methods
CASP
Clustering proteins
Comparative modelling
Databases
Disorder
Editorial
Evaluation of prediction methods
Evolution
Experimental structure
Flexibility
Genomics predictions
Membrane regions
Methods
Non-coding RNA
Prediction services
Protein binding
Protein function
Protein structure
Protein-protein interaction
Protemic predictions
Reviews
Secondary structure
Sequence analysis
Solvent accessibility
Structural genomics
Subcellular localization
Threading (remote homology)
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Publications by Category (topic/keyword)
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Alignment methods:
abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material
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Powerful fusion: PSI-BLAST and consensus sequences
D Przybylski, B Rost
Bioinformatics, 2008. Powerful fusion: PSI-BLAST and consensus sequences:in press (abstr, web, Google Scholar)
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Powerful fusion: PSI-BLAST and consensus sequences
D Przybylski, B Rost
Bioinformatics, 2008. Powerful fusion: PSI-BLAST and consensus sequences:in press (abstr, web, Google Scholar)
|
Powerful fusion: PSI-BLAST and consensus sequences
D Przybylski, B Rost
Bioinformatics, 2008. Powerful fusion: PSI-BLAST and consensus sequences:in press (abstr, web, Google Scholar)
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Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments
D Przybylski, B Rost
Nucleic Acids Research, 2007. 35:2238-2246 (abstr, web, pdf, som, Google Scholar)
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Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments
D Przybylski, B Rost
Nucleic Acids Research, 2007. 35:2238-2246 (abstr, web, pdf, som, Google Scholar)
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Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments
D Przybylski, B Rost
Nucleic Acids Research, 2007. 35:2238-2246 (abstr, web, pdf, som, Google Scholar)
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Distinguishing protein-coding from non-coding RNA through support vector machines
J Liu, J Gough, B Rost
PLoS Genetics, 2006. 2:e29; DOI: 10.1371/journal.pgen.0020029 (abstr, web, pdf, Google Scholar)
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Improving fold recognition without folds
D Przybylski, B Rost
Journal of Molecular Biology, 2004. 341:255-269 (abstr, web, pdf, Google Scholar)
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CHOP: parsing proteins into structural domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:W569-W571 (abstr, web, pdf, Google Scholar)
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CHOP proteins into structural domains
J Liu, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 55:678-688 (abstr, web, pdf, Google Scholar)
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Automatic target selection for structural genomics on eukaryotes
J Liu, H Hegyi, TB Acton, GT Montelione, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 56:188-200 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
|
Enzyme function less conserved than anticipated
B Rost
Journal of Molecular Biology, 2002. 318:595-608 (abstr, web, pdf, Google Scholar)
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Alignments grow, secondary structure prediction improves
D Przybylski, B Rost
Proteins: Structure, Function, and Bioinformatics, 2002. 46:195-205 (abstr, web, pdf, Google Scholar)
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Alignments grow, secondary structure prediction improves
D Przybylski, B Rost
Proteins: Structure, Function, and Bioinformatics, 2002. 46:195-205 (abstr, web, pdf, Google Scholar)
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Twilight zone of protein sequence alignments
B Rost
Protein Engineering, 1999. 12:85-94 (abstr, web, pdf, Google Scholar)
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Twilight zone of protein sequence alignments
B Rost
Protein Engineering, 1999. 12:85-94 (abstr, web, pdf, Google Scholar)
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Topology prediction for helical transmembrane proteins at 86% accuracy
B Rost, R Casadio, P Fariselli
Protein Science, 1996. 5:1704-1718 (abstr, web, pdf, Google Scholar)
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Refining neural network predictions for helical transmembrane proteins by dynamic programming
B Rost, R Casadio, P Fariselli
in: 'Fourth International Conference on Intelligent Systems for Molecular Biology' (eds. D States, P Agarwal, T Gaasterland, L Hunter, RF Smith), 1996. : St. Louis, M.O., U.S.A.Refining neural network predictions for helical transmembrane proteins by dynamic programming:192-200 (abstr, pdf, Google Scholar)
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PHD: predicting one-dimensional protein structure by profile based neural networks
B Rost
Methods in Enzymology, 1996. 266:525-539 (abstr, web, pdf, Google Scholar)
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TOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures
B Rost
in: 'Third International Conference on Intelligent Systems for Molecular Biology' (eds. C Rawlings, D Clark, R Altman, L Hunter, T Lengauer, S Wodak), 1995. : Cambridge, EnglandTOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures:314-321 (abstr, pdf, Google Scholar)
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Combining evolutionary information and neural networks to predict protein secondary structure
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1994. 19:55-72 (abstr, pdf, Google Scholar)
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Progress in protein structure prediction?
B Rost, C Sander, R Schneider
Trends in Biochemical Sciences, 1993. 18:120-123 (abstr, pdf, Google Scholar)
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Prediction of protein secondary structure at better than 70% accuracy
B Rost, C Sander
Journal of Molecular Biology, 1993. 232:584-599 (abstr, web, pdf, Google Scholar)
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Improved prediction of protein secondary structure by use of sequence profiles and neural networks
B Rost, C Sander
Proceedings of the National Academy of Sciences, 1993. 90:7558-7562 (abstr, pdf, Google Scholar)
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CASP:
abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material
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Critical assessment of methods of protein structure prediction (CASP)-Round 6
J Moult, K Fidelis, B Rost, T Hubbard, A Tramontano
Proteins, 2005. 61:3-7 (pdf, Google Scholar)
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CASP6 assessment of contact prediction
O Grana, D Baker, RM Maccallum, J Meiler, M Punta, B Rost, ML Tress, A Valencia
Proteins, 2005. 61:214-224 (pdf, Google Scholar)
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CASP6 assessment of contact prediction
O Grana, D Baker, RM Maccallum, J Meiler, M Punta, B Rost, ML Tress, A Valencia
Proteins, 2005. 61:214-224 (pdf, Google Scholar)
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CAFASP3 in the spotlight of EVA
VA Eyrich, IYY Koh, D Przybylski, O Graña, F Pazos, A Valencia, B Rost
Proteins: Structure, Function, and Bioinformatics, 2003. 53 Suppl 6:548-560 (abstr, web, pdf, Google Scholar)
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Simple jury predicts protein secondary structure best
B Rost, P Baldi, G Barton, J Cuff, V Eyrich, D Jones, K Karplus, R King, M Ouali, G Pollastri, D Przybylski
CUBIC preprint, 2001. Simple jury predicts protein secondary structure best:5 (abstr, web, pdf, Google Scholar)
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EVA: large-scale analysis of secondary structure prediction
B Rost, V Eyrich
Proteins: Structure, Function, and Genetics, 2001. 45 Suppl 5:S192-S199 (abstr, web, pdf, Google Scholar)
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CAFASP2: the second critical assessment of fully automated structure prediction methods
D Fischer, A Elofsson, L Rychlewski, F Pazos, A Valencia, B Rost, AR Ortiz, RLJ Dunbrack
Proteins: Structure, Function, and Genetics, 2001. 45 Suppl 5:S171-S183 (pdf, Google Scholar)
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CAFASP-1: critical assessment of fully automated structure prediction methods
D Fischer, C Barret, K Bryson, A Elofsson, A Godzik, D Jones, KJ Karplus, LA Kelley, RM MacCallum, K Pawowski, B Rost, L Rychlewski, M Sternberg
Proteins: Structure, Function, and Genetics, 1999. Suppl 3:209-217 (pdf, Google Scholar)
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Better 1D predictions by experts with machines
B Rost
Proteins: Structure, Function, and Genetics, 1997. Suppl. 1:192-197 (abstr, web, pdf, Google Scholar)
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Clustering proteins:
abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material
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Powerful fusion: PSI-BLAST and consensus sequences
D Przybylski, B Rost
Bioinformatics, 2008. Powerful fusion: PSI-BLAST and consensus sequences:in press (abstr, web, Google Scholar)
|
Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments
D Przybylski, B Rost
Nucleic Acids Research, 2007. 35:2238-2246 (abstr, web, pdf, som, Google Scholar)
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Novel leverage of structural genomics
J Liu, GT Montelione, B Rost
Nature Biotechnology, 2007. Novel leverage of structural genomics:in press (abstr, web, som, Google Scholar)
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Distinguishing protein-coding from non-coding RNA through support vector machines
J Liu, J Gough, B Rost
PLoS Genetics, 2006. 2:e29; DOI: 10.1371/journal.pgen.0020029 (abstr, web, pdf, Google Scholar)
|
Improving fold recognition without folds
D Przybylski, B Rost
Journal of Molecular Biology, 2004. 341:255-269 (abstr, web, pdf, Google Scholar)
|
Sequence-based prediction of protein domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:3522-3530 (abstr, web, pdf, Google Scholar)
|
CHOP: parsing proteins into structural domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:W569-W571 (abstr, web, pdf, Google Scholar)
|
CHOP proteins into structural domains
J Liu, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 55:678-688 (abstr, web, pdf, Google Scholar)
|
CHOP proteins into structural domains
J Liu, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 55:678-688 (abstr, web, pdf, Google Scholar)
|
Automatic target selection for structural genomics on eukaryotes
J Liu, H Hegyi, TB Acton, GT Montelione, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 56:188-200 (abstr, web, pdf, Google Scholar)
|
Automatic target selection for structural genomics on eukaryotes
J Liu, H Hegyi, TB Acton, GT Montelione, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 56:188-200 (abstr, web, pdf, Google Scholar)
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Automatic target selection for structural genomics on eukaryotes
J Liu, H Hegyi, TB Acton, GT Montelione, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 56:188-200 (abstr, web, pdf, Google Scholar)
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Prediction of transmembrane beta-barrels for entire proteomes
H Bigelow, D Petrey, J Liu, D Przybylski, B Rost
Nucleic Acids Research, 2004. 32:2566-2577 (abstr, web, pdf, Google Scholar)
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Domains, motifs, and clusters in the protein universe
J Liu, B Rost
Current Opinion in Chemical Biology, 2003. 7:5-11 (abstr, web, pdf, Google Scholar)
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Domains, motifs, and clusters in the protein universe
J Liu, B Rost
Current Opinion in Chemical Biology, 2003. 7:5-11 (abstr, web, pdf, Google Scholar)
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Comparative modelling:
abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material
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Novel leverage of structural genomics
J Liu, GT Montelione, B Rost
Nature Biotechnology, 2007. Novel leverage of structural genomics:in press (abstr, web, som, Google Scholar)
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Outcome of a workshop on archiving structural models of biological macromolecules
HM Berman, et al.
Structure, 2006. 14:1211-1217 (pdf, Google Scholar)
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Improving fold recognition without folds
D Przybylski, B Rost
Journal of Molecular Biology, 2004. 341:255-269 (abstr, web, pdf, Google Scholar)
|
The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
|
Reliability of assessment of protein structure prediction methods
MA Marti-Renom, MS Madhusudhan, A Fiser, B Rost, A Sali
Structure, 2002. 10:435-440 (pdf, Google Scholar)
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Data based modeling of proteins
L Holm, B Rost, C Sander, R Schneider, G Vriend
in: 'Statistical Mechanics, Protein Structure, and Protein Substrate Interactions' (eds. S Doniach), 1994. : New YorkData based modeling of proteins:277-296
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Molecular modelling of the Norrie disease protein predicts a cysteine knot growth factor tertiary structure
T Meitinger, A Meindl, P Bork, B Rost, C Sander, M Haasemann, J Murken
Nature Genetics, 1993. 5:376-380 (pdf, Google Scholar)
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Databases:
abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material
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Powerful fusion: PSI-BLAST and consensus sequences
D Przybylski, B Rost
Bioinformatics, 2008. Powerful fusion: PSI-BLAST and consensus sequences:in press (abstr, web, Google Scholar)
|
Powerful fusion: PSI-BLAST and consensus sequences
D Przybylski, B Rost
Bioinformatics, 2008. Powerful fusion: PSI-BLAST and consensus sequences:in press (abstr, web, Google Scholar)
|
Critical assessment of methods of protein structure prediction-Round VII
J Moult, K Fidelis, A Kryshtafovych, B Rost, T Hubbard, A Tramontano
Proteins, 2007. 69 Suppl 8:3-9
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Epitome: Database of structure-inferred antigenic epitopes
A Schlessinger, Y Ofran, G Yachdav, B Rost
Nucleic Acids Research, 2006. 34:D777-780 (abstr, web, pdf, Google Scholar)
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NMPdb: database of nuclear matrix proteins
S Mika, B Rost
Nucleic Acids Research, 2005. 33:D160-163 (abstr, web, pdf, Google Scholar)
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The protein target list of the Northeast Structural Genomics Consortium
Z Wunderlich, TB Acton, J Liu, G Kornhaber, J Everett, P Carter, N Lan, N Echols, M Gerstein, B Rost, GT Montelione
Proteins: Structure, Function, and Bioinformatics, 2004. 56:181-187 (abstr, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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NLSdb: database of nuclear localization signals
R Nair, P Carter, B Rost
Nucleic Acids Research, 2003. 31:397-399 (abstr, web, Google Scholar)
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PEP: Predictions for Entire Proteomes
P Carter, J Liu, B Rost
Nucleic Acids Research, 2003. 31:410-413 (abstr, web, pdf, Google Scholar)
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Twilight zone of protein sequence alignments
B Rost
Protein Engineering, 1999. 12:85-94 (abstr, web, pdf, Google Scholar)
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Marrying structure and genomics
B Rost
Structure, 1998. 6:259-263 (abstr, web, pdf, Google Scholar)
|
TOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures
B Rost
in: 'Third International Conference on Intelligent Systems for Molecular Biology' (eds. C Rawlings, D Clark, R Altman, L Hunter, T Lengauer, S Wodak), 1995. : Cambridge, EnglandTOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures:314-321 (abstr, pdf, Google Scholar)
|
Disorder:
abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material
|
Natively unstructured regions in proteins identified from contact predictions
A Schlessinger, M Punta, B Rost
Bioinformatics, 2007. 23:2376-2384 (abstr, web, pdf, som, Google Scholar)
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Natively unstructured regions in proteins identified from contact predictions
A Schlessinger, M Punta, B Rost
Bioinformatics, 2007. 23:2376-2384 (abstr, web, pdf, som, Google Scholar)
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Natively unstructured loops differ from other loops
A Schlessinger, J Liu, B Rost
PLoS Computational Biology, 2007. 3:e140 (abstr, web, pdf, som, Google Scholar)
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Natively unstructured loops differ from other loops
A Schlessinger, J Liu, B Rost
PLoS Computational Biology, 2007. 3:e140 (abstr, web, pdf, som, Google Scholar)
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Natively unstructured loops differ from other loops
A Schlessinger, J Liu, B Rost
PLoS Computational Biology, 2007. 3:e140 (abstr, web, pdf, som, Google Scholar)
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PROFbval: predict flexible and rigid residues in proteins
A Schlessinger, G Yachdav, B Rost
Bioinformatics, 2006. 22:891-893 (abstr, web, pdf, som, Google Scholar)
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How to use protein 1D structure predicted by PROFphd
B Rost
in: 'The Proteomics Protocols Handbook' (eds. JE Walker), 2005. : Totowa NJHow to use protein 1D structure predicted by PROFphd:875-901
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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Loopy proteins appear conserved in evolution
J Liu, H Tan, B Rost
Journal of Molecular Biology, 2002. 322:53-64 (abstr, web, pdf, Google Scholar)
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Editorial:
abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material
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ISMB 2008 Toronoto
M Linial, JP Mesirov, B Morrison McKay, B Rost
PLoS Computational Biology, 2008. 4:e1000094 (abstr, web, Google Scholar)
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ISMB/ECCB 2007: The premier conference on computational biology
T Lengauer, B Morrison McKay, B Rost
PLoS Computational Biology, 2007. 3:e96 (pdf, Google Scholar)
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ISMB/ECCB 2007
T Lengauer, B Rost, P Schuster
Bioinformatics, 2007. 23:i1-i4 (pdf, Google Scholar)
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Critical assessment of methods of protein structure prediction (CASP)-Round 6
J Moult, K Fidelis, B Rost, T Hubbard, A Tramontano
Proteins, 2005. 61:3-7 (pdf, Google Scholar)
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ISMB 2005
HV Jagadish, D States, B Rost
Bioinformatics, 2005. 21 Suppl 1:i1-i2 (pdf, Google Scholar)
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The protein target list of the Northeast Structural Genomics Consortium
Z Wunderlich, TB Acton, J Liu, G Kornhaber, J Everett, P Carter, N Lan, N Echols, M Gerstein, B Rost, GT Montelione
Proteins: Structure, Function, and Bioinformatics, 2004. 56:181-187 (abstr, pdf, Google Scholar)
|
AI and Bioinformatics
J Glasgow, I Jurisica, B Rost
AI Magazine, 2004. 25:7-8
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Bioinformatics in structural genomics
B Rost, B Honig, A Valencia
Bioinformatics, 2002. 18:897 (abstr, web, pdf, Google Scholar)
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Ismb 2002
J Glasgow, B Rost
Bioinformatics, 2002. 18:S1 (pdf, Google Scholar)
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Evaluation of prediction methods:
abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material
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Predicting simplified features of protein structure
D Przybylski, B Rost
in: 'Bioinformatics – From Genomes to Therapies' (eds. T Lengauer), 2008. : WeinheimPredicting simplified features of protein structure:in press (abstr, web, Google Scholar)
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Predicting protein subcellular localization using intelligent systems
R Nair, B Rost
Methods Mol Biol, 2008. 484:435-463 (abstr, web, Google Scholar)
|
Critical assessment of methods of protein structure prediction-Round VII
J Moult, K Fidelis, A Kryshtafovych, B Rost, T Hubbard, A Tramontano
Proteins, 2007. 69 Suppl 8:3-9
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Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
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How to use protein 1D structure predicted by PROFphd
B Rost
in: 'The Proteomics Protocols Handbook' (eds. JE Walker), 2005. : Totowa NJHow to use protein 1D structure predicted by PROFphd:875-901
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Critical assessment of methods of protein structure prediction (CASP)-Round 6
J Moult, K Fidelis, B Rost, T Hubbard, A Tramontano
Proteins, 2005. 61:3-7 (pdf, Google Scholar)
|
EVAcon: a protein contact prediction evaluation service
O Grana, VA Eyrich, F Pazos, B Rost, A Valencia
Nucleic Acids Res, 2005. 33:W347-51 (pdf, Google Scholar)
|
CASP6 assessment of contact prediction
O Grana, D Baker, RM Maccallum, J Meiler, M Punta, B Rost, ML Tress, A Valencia
Proteins, 2005. 61:214-224 (pdf, Google Scholar)
|
The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
|
CAFASP3 in the spotlight of EVA
VA Eyrich, IYY Koh, D Przybylski, O Graña, F Pazos, A Valencia, B Rost
Proteins: Structure, Function, and Bioinformatics, 2003. 53 Suppl 6:548-560 (abstr, web, pdf, Google Scholar)
|
Enzyme function less conserved than anticipated
B Rost
Journal of Molecular Biology, 2002. 318:595-608 (abstr, web, pdf, Google Scholar)
|
Reliability of assessment of protein structure prediction methods
MA Marti-Renom, MS Madhusudhan, A Fiser, B Rost, A Sali
Structure, 2002. 10:435-440 (pdf, Google Scholar)
|
Transmembrane helix predictions revisited
CP Chen, A Kernytsky, B Rost
Protein Science, 2002. 11:2774-2791 (abstr, web, pdf, Google Scholar)
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State-of-the-art in membrane prediction
CP Chen, B Rost
Applied Bioinformatics, 2002. 1:21-35 (abstr, web, pdf, Google Scholar)
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Long membrane helices and short loops predicted less accurately
CP Chen, B Rost
Protein Science, 2002. Long membrane helices and short loops predicted less accurately:2766-2773 (abstr, web, pdf, Google Scholar)
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Simple jury predicts protein secondary structure best
B Rost, P Baldi, G Barton, J Cuff, V Eyrich, D Jones, K Karplus, R King, M Ouali, G Pollastri, D Przybylski
CUBIC preprint, 2001. Simple jury predicts protein secondary structure best:5 (abstr, web, pdf, Google Scholar)
|
EVA: large-scale analysis of secondary structure prediction
B Rost, V Eyrich
Proteins: Structure, Function, and Genetics, 2001. 45 Suppl 5:S192-S199 (abstr, web, pdf, Google Scholar)
|
CAFASP2: the second critical assessment of fully automated structure prediction methods
D Fischer, A Elofsson, L Rychlewski, F Pazos, A Valencia, B Rost, AR Ortiz, RLJ Dunbrack
Proteins: Structure, Function, and Genetics, 2001. 45 Suppl 5:S171-S183 (pdf, Google Scholar)
|
EVA: continuous automatic evaluation of protein structure prediction servers
V Eyrich, MA Martí-Renom, D Przybylski, A Fiser, F Pazos, A Valencia, A Sali, B Rost
Bioinformatics, 2001. 17:1242-1243 (abstr, web, pdf, Google Scholar)
|
A modified definition of SOV, a segment-based measure for protein secondary structure prediction assessment
A Zemla, C Venclovas, K Fidelis, B Rost
Proteins: Structure, Function, and Genetics, 1999. 34:220-223 (pdf, Google Scholar)
|
Protein fold recognition by prediction-based threading
B Rost, R Schneider, C Sander
Journal of Molecular Biology, 1997. 270:471-480 (abstr, web, pdf, Google Scholar)
|
TOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures
B Rost
in: 'Third International Conference on Intelligent Systems for Molecular Biology' (eds. C Rawlings, D Clark, R Altman, L Hunter, T Lengauer, S Wodak), 1995. : Cambridge, EnglandTOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures:314-321 (abstr, pdf, Google Scholar)
|
Progress of 1D protein structure prediction at last
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1995. 23:295-300 (abstr, pdf, Google Scholar)
|
Redefining the goals of protein secondary structure prediction
B Rost, C Sander, R Schneider
Journal of Molecular Biology, 1994. 235:13-26 (abstr, pdf, Google Scholar)
|
Evolution:
abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material
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Natively unstructured regions in proteins identified from contact predictions
A Schlessinger, M Punta, B Rost
Bioinformatics, 2007. 23:2376-2384 (abstr, web, pdf, som, Google Scholar)
|
Natively unstructured loops differ from other loops
A Schlessinger, J Liu, B Rost
PLoS Computational Biology, 2007. 3:e140 (abstr, web, pdf, som, Google Scholar)
|
Protein-protein interaction hot spots carved into sequences
Y Ofran, B Rost
PLoS Computational Biology, 2007. 3:e119 (abstr, web, pdf, Google Scholar)
|
Prediction of DNA-binding residues from sequence
Y Ofran, V Mysore, B Rost
Bioinformatics, 2007. 23:i347-i353 (abstr, web, pdf, Google Scholar)
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ISIS: Interaction Sites Identified from Sequence
Y Ofran, B Rost
Bioinformatics, 2007. 23:e13-16 (abstr, web, pdf, Google Scholar)
|
SNAP: predict effect of non-synonymous polymorphisms on function
Y Bromberg, B Rost
Nucleic Acids Research, 2007. 35:3823-3835 (abstr, web, pdf, Google Scholar)
|
PROFbval: predict flexible and rigid residues in proteins
A Schlessinger, G Yachdav, B Rost
Bioinformatics, 2006. 22:891-893 (abstr, web, pdf, som, Google Scholar)
|
Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
|
Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
|
Protein–protein interactions more conserved within species than across species
S Mika, B Rost
PLoS Computational Biology, 2006. 2:e79 (abstr, web, pdf, som, Google Scholar)
|
Protein–protein interactions more conserved within species than across species
S Mika, B Rost
PLoS Computational Biology, 2006. 2:e79 (abstr, web, pdf, som, Google Scholar)
|
How to use protein 1D structure predicted by PROFphd
B Rost
in: 'The Proteomics Protocols Handbook' (eds. JE Walker), 2005. : Totowa NJHow to use protein 1D structure predicted by PROFphd:875-901
|
PROFcon: novel prediction of long-range contacts
M Punta, B Rost
Bioinformatics, 2005. 21:2960-2968 (abstr, web, pdf, Google Scholar)
|
Mimicking cellular sorting improves prediction of subcellular localization
R Nair, B Rost
Journal of Molecular Biology, 2005. 348:85-100 (abstr, web, pdf, Google Scholar)
|
LOCnet and LOCtarget: Sub-cellular localization for structural genomics targets
R Nair, B Rost
Nucleic Acids Research, 2004. 32:W517-W521 (abstr, web, pdf, Google Scholar)
|
Annotating protein function through lexical analysis
R Nair, B Rost
AI Magazine, 2004. 25:45-56 (abstr, web, Google Scholar)
|
Sequence-based prediction of protein domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:3522-3530 (abstr, web, pdf, Google Scholar)
|
The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
|
Better prediction of sub-cellular localization by combining evolutionary and structural information
R Nair, B Rost
Proteins: Structure, Function, and Bioinformatics, 2003. 53:917-930 (abstr, web, pdf, Google Scholar)
|
Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles
G Pollastri, D Przybylski, B Rost, P Baldi
Proteins: Structure, Function, and Bioinformatics, 2002. 47:228-235 (pdf, Google Scholar)
|
Sequence conserved for sub-cellular localization
R Nair, B Rost
Protein Science, 2002. 11:2836-2847 (abstr, web, pdf, Google Scholar)
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Protein structures sustain evolutionary drift
B Rost
Folding & Design, 1997. 2:S19-S24 (abstr, web, pdf, Google Scholar)
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Topology prediction for helical transmembrane proteins at 86% accuracy
B Rost, R Casadio, P Fariselli
Protein Science, 1996. 5:1704-1718 (abstr, web, pdf, Google Scholar)
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Refining neural network predictions for helical transmembrane proteins by dynamic programming
B Rost, R Casadio, P Fariselli
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Combining evolutionary information and neural networks to predict protein secondary structure
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1994. 19:55-72 (abstr, pdf, Google Scholar)
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Progress in protein structure prediction?
B Rost, C Sander, R Schneider
Trends in Biochemical Sciences, 1993. 18:120-123 (abstr, pdf, Google Scholar)
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Prediction of protein secondary structure at better than 70% accuracy
B Rost, C Sander
Journal of Molecular Biology, 1993. 232:584-599 (abstr, web, pdf, Google Scholar)
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Experimental structure:
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NMR structure of the peptidyl-tRNA hydrolase domain from Pseudomonas syringae expands the structural coverage of the hydrolysis domains of class 1 peptide chain release factors
KK Singarapu, R Xiao, T Acton, B Rost, GT Montelione, T Szyperski
Proteins: Structure, Function, and Bioinformatics, 2008. 71:1027-1031 (pdf, Google Scholar)
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Solution NMR structure of the SOS response protein YnzC from Bacillus subtilis
JM Aramini, S Sharma, YJ Huang, GV Swapna, CK Ho, K Shetty, K Cunningham, LC Ma, L Zhao, LA Owens, M Jiang, R Xiao, J Liu, MC Baran, TB Acton, B Rost, GT Montelione
Proteins: Structure, Function, and Genetics, 2008. 72:526-530 (pdf, Google Scholar)
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Solution NMR structure of Escherichia coli ytfP expands the structural coverage of the UPF0131 protein domain family
JM Aramini, YJ Huang, GV Swapna, JR Cort, PK Rajan, R Xiao, R Shastry, TB Acton, J Liu, B Rost, MA Kennedy, GT Montelione
Proteins, 2007. 68:789-95 (pdf, Google Scholar)
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Solution structure of Archaeglobus fulgidis peptidyl-tRNA hydrolase (Pth2) provides evidence for an extensive conserved family of Pth2 enzymes in archea, bacteria, and eukaryotes
R Powers, N Mirkovic, S Goldsmith-Fischman, TB Acton, Y Chiang, YJ Huang, L Ma, PK Rajan, JR Cort, MA Kennedy, J Liu, B Rost, B Honig, D Murray, GT Montelione
Protein Science, 2005. 14:2849-61 (pdf, Google Scholar)
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The protein target list of the Northeast Structural Genomics Consortium
Z Wunderlich, TB Acton, J Liu, G Kornhaber, J Everett, P Carter, N Lan, N Echols, M Gerstein, B Rost, GT Montelione
Proteins: Structure, Function, and Bioinformatics, 2004. 56:181-187 (abstr, pdf, Google Scholar)
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1H, 13C and 15N assignments for the Archaeglobus fulgidis protein AF2095
R Powers, TB Acton, Y Chiang, PK Rajan, JR Cort, MA Kennedy, J Liu, L Ma, B Rost, GT Montelione
Journal of Biomolecular NMR, 2004. 30:107-108 (pdf, Google Scholar)
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Flexibility:
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Protein conformational flexibility prediction using machine learning
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J Magn Reson, 2008. 192:37-47 (pdf, Google Scholar)
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Secondary structure assignment
CAF Andersen, B Rost
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PROFbval: predict flexible and rigid residues in proteins
A Schlessinger, G Yachdav, B Rost
Bioinformatics, 2006. 22:891-893 (abstr, web, pdf, som, Google Scholar)
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PROFbval: predict flexible and rigid residues in proteins
A Schlessinger, G Yachdav, B Rost
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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DSSPcont: continuous secondary structure assignments for proteins
P Carter, CAF Andersen, B Rost
Nucleic Acids Research, 2003. 31:3293-3295 (abstr, web, pdf, Google Scholar)
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Automatic secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2003. 44:341-363 (abstr, web, Google Scholar)
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Continuum secondary structure captures protein flexibility
CAF Andersen, AG Palmer, S Brunak, B Rost
Structure, 2002. 10:175-184 (abstr, web, pdf, Google Scholar)
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Genomics predictions:
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NMR structure of the peptidyl-tRNA hydrolase domain from Pseudomonas syringae expands the structural coverage of the hydrolysis domains of class 1 peptide chain release factors
KK Singarapu, R Xiao, T Acton, B Rost, GT Montelione, T Szyperski
Proteins: Structure, Function, and Bioinformatics, 2008. 71:1027-1031 (pdf, Google Scholar)
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Solution NMR structure of the SOS response protein YnzC from Bacillus subtilis
JM Aramini, S Sharma, YJ Huang, GV Swapna, CK Ho, K Shetty, K Cunningham, LC Ma, L Zhao, LA Owens, M Jiang, R Xiao, J Liu, MC Baran, TB Acton, B Rost, GT Montelione
Proteins: Structure, Function, and Genetics, 2008. 72:526-530 (pdf, Google Scholar)
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Secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2008. Secondary structure assignment:in press (abstr, web, Google Scholar)
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Novel leverage of structural genomics
J Liu, GT Montelione, B Rost
Nature Biotechnology, 2007. Novel leverage of structural genomics:in press (abstr, web, som, Google Scholar)
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Solution NMR structure of Escherichia coli ytfP expands the structural coverage of the UPF0131 protein domain family
JM Aramini, YJ Huang, GV Swapna, JR Cort, PK Rajan, R Xiao, R Shastry, TB Acton, J Liu, B Rost, MA Kennedy, GT Montelione
Proteins, 2007. 68:789-95 (pdf, Google Scholar)
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Solution structure of Archaeglobus fulgidis peptidyl-tRNA hydrolase (Pth2) provides evidence for an extensive conserved family of Pth2 enzymes in archea, bacteria, and eukaryotes
R Powers, N Mirkovic, S Goldsmith-Fischman, TB Acton, Y Chiang, YJ Huang, L Ma, PK Rajan, JR Cort, MA Kennedy, J Liu, B Rost, B Honig, D Murray, GT Montelione
Protein Science, 2005. 14:2849-61 (pdf, Google Scholar)
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The transcriptional landscape of the mammalian genome
P Carninci, et al.
Science, 2005. 309:1559-1563 (pdf, Google Scholar)
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The transcriptional landscape of the mammalian genome
P Carninci, et al.
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The protein target list of the Northeast Structural Genomics Consortium
Z Wunderlich, TB Acton, J Liu, G Kornhaber, J Everett, P Carter, N Lan, N Echols, M Gerstein, B Rost, GT Montelione
Proteins: Structure, Function, and Bioinformatics, 2004. 56:181-187 (abstr, pdf, Google Scholar)
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1H, 13C and 15N assignments for the Archaeglobus fulgidis protein AF2095
R Powers, TB Acton, Y Chiang, PK Rajan, JR Cort, MA Kennedy, J Liu, L Ma, B Rost, GT Montelione
Journal of Biomolecular NMR, 2004. 30:107-108 (pdf, Google Scholar)
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LOCnet and LOCtarget: Sub-cellular localization for structural genomics targets
R Nair, B Rost
Nucleic Acids Research, 2004. 32:W517-W521 (abstr, web, pdf, Google Scholar)
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Annotating protein function through lexical analysis
R Nair, B Rost
AI Magazine, 2004. 25:45-56 (abstr, web, Google Scholar)
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Sequence-based prediction of protein domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:3522-3530 (abstr, web, pdf, Google Scholar)
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Sequence-based prediction of protein domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:3522-3530 (abstr, web, pdf, Google Scholar)
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CHOP: parsing proteins into structural domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:W569-W571 (abstr, web, pdf, Google Scholar)
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CHOP proteins into structural domains
J Liu, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 55:678-688 (abstr, web, pdf, Google Scholar)
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CHOP proteins into structural domains
J Liu, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 55:678-688 (abstr, web, pdf, Google Scholar)
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Automatic target selection for structural genomics on eukaryotes
J Liu, H Hegyi, TB Acton, GT Montelione, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 56:188-200 (abstr, web, pdf, Google Scholar)
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Automatic target selection for structural genomics on eukaryotes
J Liu, H Hegyi, TB Acton, GT Montelione, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 56:188-200 (abstr, web, pdf, Google Scholar)
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Automatic target selection for structural genomics on eukaryotes
J Liu, H Hegyi, TB Acton, GT Montelione, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 56:188-200 (abstr, web, pdf, Google Scholar)
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Prediction of transmembrane beta-barrels for entire proteomes
H Bigelow, D Petrey, J Liu, D Przybylski, B Rost
Nucleic Acids Research, 2004. 32:2566-2577 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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Prediction in 1D: secondary structure, membrane helices, and accessibility
B Rost
Methods Biochem Anal, 2003. 44:559-587 (abstr, web, Google Scholar)
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Solution NMR structure of the 30S ribosomal protein S28E from Pyrococcus horikoshii
JM Aramini, YJ Huang, JR Cort, S Goldsmith-Fischman, R Xiao, LY Shih, CK Ho, J Liu, B Rost, B Honig, MA Kennedy, TB Acton, GT Montelione
Protein Science, 2003. 12:2823-2830 (abstr, pdf, Google Scholar)
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Automatic secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2003. 44:341-363 (abstr, web, Google Scholar)
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Target space for structural genomics revisited
J Liu, B Rost
Bioinformatics, 2002. 18:922-933 (abstr, web, pdf, Google Scholar)
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Comparing function and structure between entire proteomes
J Liu, B Rost
Protein Science, 2001. 10:1970-1979 (abstr, web, pdf, Google Scholar)
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Marrying structure and genomics
B Rost
Structure, 1998. 6:259-263 (abstr, web, pdf, Google Scholar)
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Membrane regions:
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Prediction of protein structure in 1D – Secondary structure, membrane regions, and solvent accessibility
B Rost
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Prediction of protein structure in 1D – Secondary structure, membrane regions, and solvent accessibility
B Rost
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Prediction of protein structure in 1D – Secondary structure, membrane regions, and solvent accessibility
B Rost
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Prediction of protein structure in 1D – Secondary structure, membrane regions, and solvent accessibility
B Rost
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Online tools for predicting integral membrane proteins
H Bigelow, B Rost
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Online tools for predicting integral membrane proteins
H Bigelow, B Rost
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Online tools for predicting integral membrane proteins
H Bigelow, B Rost
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Online tools for predicting integral membrane proteins
H Bigelow, B Rost
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Online tools for predicting integral membrane proteins
H Bigelow, B Rost
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Online tools for predicting integral membrane proteins
H Bigelow, B Rost
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Online tools for predicting integral membrane proteins
H Bigelow, B Rost
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Membrane protein prediction methods
M Punta, LR Forrest, H Bigelow, A Kernytsky, J Liu, B Rost
Methods, 2007. 41:460-474 (pdf, Google Scholar)
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Membrane protein prediction methods
M Punta, LR Forrest, H Bigelow, A Kernytsky, J Liu, B Rost
Methods, 2007. 41:460-474 (pdf, Google Scholar)
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Membrane protein prediction methods
M Punta, LR Forrest, H Bigelow, A Kernytsky, J Liu, B Rost
Methods, 2007. 41:460-474 (pdf, Google Scholar)
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Membrane protein prediction methods
M Punta, LR Forrest, H Bigelow, A Kernytsky, J Liu, B Rost
Methods, 2007. 41:460-474 (pdf, Google Scholar)
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Membrane protein prediction methods
M Punta, LR Forrest, H Bigelow, A Kernytsky, J Liu, B Rost
Methods, 2007. 41:460-474 (pdf, Google Scholar)
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Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
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Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
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Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
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Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
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Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
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Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
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PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins
H Bigelow, B Rost
Nucleic Acids Research, 2006. 34:W186-188 (abstr, web, pdf, Google Scholar)
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PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins
H Bigelow, B Rost
Nucleic Acids Research, 2006. 34:W186-188 (abstr, web, pdf, Google Scholar)
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PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins
H Bigelow, B Rost
Nucleic Acids Research, 2006. 34:W186-188 (abstr, web, pdf, Google Scholar)
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PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins
H Bigelow, B Rost
Nucleic Acids Research, 2006. 34:W186-188 (abstr, web, pdf, Google Scholar)
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PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins
H Bigelow, B Rost
Nucleic Acids Research, 2006. 34:W186-188 (abstr, web, pdf, Google Scholar)
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How to use protein 1D structure predicted by PROFphd
B Rost
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How to use protein 1D structure predicted by PROFphd
B Rost
in: 'The Proteomics Protocols Handbook' (eds. JE Walker), 2005. : Totowa NJHow to use protein 1D structure predicted by PROFphd:875-901
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How to use protein 1D structure predicted by PROFphd
B Rost
in: 'The Proteomics Protocols Handbook' (eds. JE Walker), 2005. : Totowa NJHow to use protein 1D structure predicted by PROFphd:875-901
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How to use protein 1D structure predicted by PROFphd
B Rost
in: 'The Proteomics Protocols Handbook' (eds. JE Walker), 2005. : Totowa NJHow to use protein 1D structure predicted by PROFphd:875-901
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How to use protein 1D structure predicted by PROFphd
B Rost
in: 'The Proteomics Protocols Handbook' (eds. JE Walker), 2005. : Totowa NJHow to use protein 1D structure predicted by PROFphd:875-901
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How to use protein 1D structure predicted by PROFphd
B Rost
in: 'The Proteomics Protocols Handbook' (eds. JE Walker), 2005. : Totowa NJHow to use protein 1D structure predicted by PROFphd:875-901
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Prediction of transmembrane beta-barrels for entire proteomes
H Bigelow, D Petrey, J Liu, D Przybylski, B Rost
Nucleic Acids Research, 2004. 32:2566-2577 (abstr, web, pdf, Google Scholar)
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Prediction of transmembrane beta-barrels for entire proteomes
H Bigelow, D Petrey, J Liu, D Przybylski, B Rost
Nucleic Acids Research, 2004. 32:2566-2577 (abstr, web, pdf, Google Scholar)
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Prediction of transmembrane beta-barrels for entire proteomes
H Bigelow, D Petrey, J Liu, D Przybylski, B Rost
Nucleic Acids Research, 2004. 32:2566-2577 (abstr, web, pdf, Google Scholar)
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Prediction of transmembrane beta-barrels for entire proteomes
H Bigelow, D Petrey, J Liu, D Przybylski, B Rost
Nucleic Acids Research, 2004. 32:2566-2577 (abstr, web, pdf, Google Scholar)
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Prediction of transmembrane beta-barrels for entire proteomes
H Bigelow, D Petrey, J Liu, D Przybylski, B Rost
Nucleic Acids Research, 2004. 32:2566-2577 (abstr, web, pdf, Google Scholar)
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Role of transmembrane domains in the functions of Fc receptors
R Zidovetzki, B Rost, DL Armstrong, I Pecht
Journal of Biophysical Chemistry, 2003. 15:555-575 (abstr, pdf, Google Scholar)
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Role of transmembrane domains in the functions of Fc receptors
R Zidovetzki, B Rost, DL Armstrong, I Pecht
Journal of Biophysical Chemistry, 2003. 15:555-575 (abstr, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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Target space for structural genomics revisited
J Liu, B Rost
Bioinformatics, 2002. 18:922-933 (abstr, web, pdf, Google Scholar)
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Transmembrane helix predictions revisited
CP Chen, A Kernytsky, B Rost
Protein Science, 2002. 11:2774-2791 (abstr, web, pdf, Google Scholar)
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Transmembrane helix predictions revisited
CP Chen, A Kernytsky, B Rost
Protein Science, 2002. 11:2774-2791 (abstr, web, pdf, Google Scholar)
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Transmembrane helix predictions revisited
CP Chen, A Kernytsky, B Rost
Protein Science, 2002. 11:2774-2791 (abstr, web, pdf, Google Scholar)
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State-of-the-art in membrane prediction
CP Chen, B Rost
Applied Bioinformatics, 2002. 1:21-35 (abstr, web, pdf, Google Scholar)
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State-of-the-art in membrane prediction
CP Chen, B Rost
Applied Bioinformatics, 2002. 1:21-35 (abstr, web, pdf, Google Scholar)
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State-of-the-art in membrane prediction
CP Chen, B Rost
Applied Bioinformatics, 2002. 1:21-35 (abstr, web, pdf, Google Scholar)
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State-of-the-art in membrane prediction
CP Chen, B Rost
Applied Bioinformatics, 2002. 1:21-35 (abstr, web, pdf, Google Scholar)
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Long membrane helices and short loops predicted less accurately
CP Chen, B Rost
Protein Science, 2002. Long membrane helices and short loops predicted less accurately:2766-2773 (abstr, web, pdf, Google Scholar)
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Long membrane helices and short loops predicted less accurately
CP Chen, B Rost
Protein Science, 2002. Long membrane helices and short loops predicted less accurately:2766-2773 (abstr, web, pdf, Google Scholar)
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Long membrane helices and short loops predicted less accurately
CP Chen, B Rost
Protein Science, 2002. Long membrane helices and short loops predicted less accurately:2766-2773 (abstr, web, pdf, Google Scholar)
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Comparing function and structure between entire proteomes
J Liu, B Rost
Protein Science, 2001. 10:1970-1979 (abstr, web, pdf, Google Scholar)
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The role of transmembrane domains in the functions of B- and T-cell receptors
R Zidovetzki, B Rost, I Pecht
Immunology Letters, 1998. 64:97-107 (abstr, web, pdf, Google Scholar)
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The role of transmembrane domains in the functions of B- and T-cell receptors
R Zidovetzki, B Rost, I Pecht
Immunology Letters, 1998. 64:97-107 (abstr, web, pdf, Google Scholar)
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Learning from evolution to predict protein structure
B Rost
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Topology prediction for helical transmembrane proteins at 86% accuracy
B Rost, R Casadio, P Fariselli
Protein Science, 1996. 5:1704-1718 (abstr, web, pdf, Google Scholar)
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Topology prediction for helical transmembrane proteins at 86% accuracy
B Rost, R Casadio, P Fariselli
Protein Science, 1996. 5:1704-1718 (abstr, web, pdf, Google Scholar)
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Topology prediction for helical transmembrane proteins at 86% accuracy
B Rost, R Casadio, P Fariselli
Protein Science, 1996. 5:1704-1718 (abstr, web, pdf, Google Scholar)
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Topology prediction for helical transmembrane proteins at 86% accuracy
B Rost, R Casadio, P Fariselli
Protein Science, 1996. 5:1704-1718 (abstr, web, pdf, Google Scholar)
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Refining neural network predictions for helical transmembrane proteins by dynamic programming
B Rost, R Casadio, P Fariselli
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Refining neural network predictions for helical transmembrane proteins by dynamic programming
B Rost, R Casadio, P Fariselli
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PHD: predicting one-dimensional protein structure by profile based neural networks
B Rost
Methods in Enzymology, 1996. 266:525-539 (abstr, web, pdf, Google Scholar)
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PHD: predicting one-dimensional protein structure by profile based neural networks
B Rost
Methods in Enzymology, 1996. 266:525-539 (abstr, web, pdf, Google Scholar)
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PHD: predicting one-dimensional protein structure by profile based neural networks
B Rost
Methods in Enzymology, 1996. 266:525-539 (abstr, web, pdf, Google Scholar)
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Prediction of helical transmembrane segments at 95% accuracy
B Rost, R Casadio, P Fariselli, C Sander
Protein Science, 1995. 4:521-533 (abstr, web, pdf, Google Scholar)
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Prediction of helical transmembrane segments at 95% accuracy
B Rost, R Casadio, P Fariselli, C Sander
Protein Science, 1995. 4:521-533 (abstr, web, pdf, Google Scholar)
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Prediction of helical transmembrane segments at 95% accuracy
B Rost, R Casadio, P Fariselli, C Sander
Protein Science, 1995. 4:521-533 (abstr, web, pdf, Google Scholar)
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Methods:
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Predicting simplified features of protein structure
D Przybylski, B Rost
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Powerful fusion: PSI-BLAST and consensus sequences
D Przybylski, B Rost
Bioinformatics, 2008. Powerful fusion: PSI-BLAST and consensus sequences:in press (abstr, web, Google Scholar)
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Powerful fusion: PSI-BLAST and consensus sequences
D Przybylski, B Rost
Bioinformatics, 2008. Powerful fusion: PSI-BLAST and consensus sequences:in press (abstr, web, Google Scholar)
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Predicting protein subcellular localization using intelligent systems
R Nair, B Rost
Methods Mol Biol, 2008. 484:435-463 (abstr, web, Google Scholar)
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Using genetic algorithms to select most predictive protein features
A Kernytsky, B Rost
Proteins: Structure, Function, and Bioinformatics, 2008. Using genetic algorithms to select most predictive protein features:in press (abstr, web, Google Scholar)
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Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments
D Przybylski, B Rost
Nucleic Acids Research, 2007. 35:2238-2246 (abstr, web, pdf, som, Google Scholar)
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ISIS: Interaction Sites Identified from Sequence
Y Ofran, B Rost
Bioinformatics, 2007. 23:e13-16 (abstr, web, pdf, Google Scholar)
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ISIS: Interaction Sites Identified from Sequence
Y Ofran, B Rost
Bioinformatics, 2007. 23:e13-16 (abstr, web, pdf, Google Scholar)
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ISIS: Interaction Sites Identified from Sequence
Y Ofran, B Rost
Bioinformatics, 2007. 23:e13-16 (abstr, web, pdf, Google Scholar)
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Critical assessment of methods of protein structure prediction-Round VII
J Moult, K Fidelis, A Kryshtafovych, B Rost, T Hubbard, A Tramontano
Proteins, 2007. 69 Suppl 8:3-9
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Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
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Distinguishing protein-coding from non-coding RNA through support vector machines
J Liu, J Gough, B Rost
PLoS Genetics, 2006. 2:e29; DOI: 10.1371/journal.pgen.0020029 (abstr, web, pdf, Google Scholar)
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How to use protein 1D structure predicted by PROFphd
B Rost
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Critical assessment of methods of protein structure prediction (CASP)-Round 6
J Moult, K Fidelis, B Rost, T Hubbard, A Tramontano
Proteins, 2005. 61:3-7 (pdf, Google Scholar)
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EVAcon: a protein contact prediction evaluation service
O Grana, VA Eyrich, F Pazos, B Rost, A Valencia
Nucleic Acids Res, 2005. 33:W347-51 (pdf, Google Scholar)
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CASP6 assessment of contact prediction
O Grana, D Baker, RM Maccallum, J Meiler, M Punta, B Rost, ML Tress, A Valencia
Proteins, 2005. 61:214-224 (pdf, Google Scholar)
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Improving fold recognition without folds
D Przybylski, B Rost
Journal of Molecular Biology, 2004. 341:255-269 (abstr, web, pdf, Google Scholar)
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Sequence-based prediction of protein domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:3522-3530 (abstr, web, pdf, Google Scholar)
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CHOP: parsing proteins into structural domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:W569-W571 (abstr, web, pdf, Google Scholar)
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CHOP proteins into structural domains
J Liu, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 55:678-688 (abstr, web, pdf, Google Scholar)
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Automatic target selection for structural genomics on eukaryotes
J Liu, H Hegyi, TB Acton, GT Montelione, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 56:188-200 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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DSSPcont: continuous secondary structure assignments for proteins
P Carter, CAF Andersen, B Rost
Nucleic Acids Research, 2003. 31:3293-3295 (abstr, web, pdf, Google Scholar)
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Non-coding RNA:
abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material
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Distinguishing protein-coding from non-coding RNA through support vector machines
J Liu, J Gough, B Rost
PLoS Genetics, 2006. 2:e29; DOI: 10.1371/journal.pgen.0020029 (abstr, web, pdf, Google Scholar)
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Distinguishing protein-coding from non-coding RNA through support vector machines
J Liu, J Gough, B Rost
PLoS Genetics, 2006. 2:e29; DOI: 10.1371/journal.pgen.0020029 (abstr, web, pdf, Google Scholar)
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Distinguishing protein-coding from non-coding RNA through support vector machines
J Liu, J Gough, B Rost
PLoS Genetics, 2006. 2:e29; DOI: 10.1371/journal.pgen.0020029 (abstr, web, pdf, Google Scholar)
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The transcriptional landscape of the mammalian genome
P Carninci, et al.
Science, 2005. 309:1559-1563 (pdf, Google Scholar)
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The transcriptional landscape of the mammalian genome
P Carninci, et al.
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Prediction services:
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Predicting simplified features of protein structure
D Przybylski, B Rost
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Predicting protein subcellular localization using intelligent systems
R Nair, B Rost
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Comprehensive in silico mutagenesis highlights functionally improtant residues in proteins
Y Bromberg, B Rost
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Online tools for predicting integral membrane proteins
H Bigelow, B Rost
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SNAP: predict effect of non-synonymous polymorphisms on function
Y Bromberg, B Rost
Nucleic Acids Research, 2007. 35:3823-3835 (abstr, web, pdf, Google Scholar)
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PROFbval: predict flexible and rigid residues in proteins
A Schlessinger, G Yachdav, B Rost
Bioinformatics, 2006. 22:891-893 (abstr, web, pdf, som, Google Scholar)
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Epitome: Database of structure-inferred antigenic epitopes
A Schlessinger, Y Ofran, G Yachdav, B Rost
Nucleic Acids Research, 2006. 34:D777-780 (abstr, web, pdf, Google Scholar)
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Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
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Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
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Protein–protein interactions more conserved within species than across species
S Mika, B Rost
PLoS Computational Biology, 2006. 2:e79 (abstr, web, pdf, som, Google Scholar)
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PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins
H Bigelow, B Rost
Nucleic Acids Research, 2006. 34:W186-188 (abstr, web, pdf, Google Scholar)
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PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins
H Bigelow, B Rost
Nucleic Acids Research, 2006. 34:W186-188 (abstr, web, pdf, Google Scholar)
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How to use protein 1D structure predicted by PROFphd
B Rost
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PROFcon: novel prediction of long-range contacts
M Punta, B Rost
Bioinformatics, 2005. 21:2960-2968 (abstr, web, pdf, Google Scholar)
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Predictive methods using protein sequence
Y Ofran, B Rost
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LOCnet and LOCtarget: Sub-cellular localization for structural genomics targets
R Nair, B Rost
Nucleic Acids Research, 2004. 32:W517-W521 (abstr, web, pdf, Google Scholar)
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Annotating protein function through lexical analysis
R Nair, B Rost
AI Magazine, 2004. 25:45-56 (abstr, web, Google Scholar)
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Protein names peeled precisely off free text
S Mika, B Rost
Bioinformatics, 2004. 20:I241-I247 (abstr, web, pdf, Google Scholar)
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NLProt: extracting protein names and sequences from papers
S Mika, B Rost
Nucleic Acids Research, 2004. 32:W634-W637 (abstr, web, pdf, Google Scholar)
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Sequence-based prediction of protein domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:3522-3530 (abstr, web, pdf, Google Scholar)
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CHOP: parsing proteins into structural domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:W569-W571 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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LOC3D: annotate sub-cellular localization for protein structures
R Nair, B Rost
Nucleic Acids Research, 2003. 31:3337-3340 (abstr, web, pdf, Google Scholar)
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UniqueProt: creating representative protein sequence sets
S Mika, B Rost
Nucleic Acids Research, 2003. 31:3789-3791 (abstr, web, pdf, Google Scholar)
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NORSp: predictions of long regions without regular secondary structure
J Liu, B Rost
Nucleic Acids Research, 2003. 31:3833-3835 (abstr, web, pdf, Google Scholar)
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EVA: evaluation of protein structure prediction servers
IYY Koh, VA Eyrich, MA Marti-Renom, D Przybylski, MS Madhusudhan, E Narayanan, O Grana, A Valencia, A Sali, B Rost
Nucleic Acids Research, 2003. 31:3311-3315 (abstr, web, pdf, Google Scholar)
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Static benchmarking of membrane helix predictions
A Kernytsky, B Rost
Nucleic Acids Research, 2003. 31:3642-3644 (abstr, web, pdf, Google Scholar)
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META-PP: single interface to crucial prediction servers
VA Eyrich, B Rost
Nucleic Acids Research, 2003. 31:3308-3310 (abstr, web, pdf, Google Scholar)
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CAFASP3 in the spotlight of EVA
VA Eyrich, IYY Koh, D Przybylski, O Graña, F Pazos, A Valencia, B Rost
Proteins: Structure, Function, and Bioinformatics, 2003. 53 Suppl 6:548-560 (abstr, web, pdf, Google Scholar)
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Protein binding:
abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material
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ISIS: Interaction Sites Identified from Sequence
Y Ofran, B Rost
Bioinformatics, 2007. 23:e13-16 (abstr, web, pdf, Google Scholar)
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ISIS: Interaction Sites Identified from Sequence
Y Ofran, B Rost
Bioinformatics, 2007. 23:e13-16 (abstr, web, pdf, Google Scholar)
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Identifying cysteines and histidines in transition-metal-binding sites using support vector machines and neural networks
A Passerini, M Punta, A Ceroni, B Rost, P Frasconi
Proteins: Structure, Function, and Bioinformatics, 2006. 65:305-316 (pdf, Google Scholar)
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Identifying cysteines and histidines in transition-metal-binding sites using support vector machines and neural networks
A Passerini, M Punta, A Ceroni, B Rost, P Frasconi
Proteins: Structure, Function, and Bioinformatics, 2006. 65:305-316 (pdf, Google Scholar)
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Protein–protein interactions more conserved within species than across species
S Mika, B Rost
PLoS Computational Biology, 2006. 2:e79 (abstr, web, pdf, som, Google Scholar)
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Protein–protein interactions more conserved within species than across species
S Mika, B Rost
PLoS Computational Biology, 2006. 2:e79 (abstr, web, pdf, som, Google Scholar)
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Predictive methods using protein sequence
Y Ofran, B Rost
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Beyond annotation transfer by homology: novel protein function prediction methods that can assist drug discovery
Y Ofran, M Punta, R Schneider, B Rost
Drug Discovery Today, 2005. 10:1475-1482 (pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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Analysing six types of protein-protein interfaces
Y Ofran, B Rost
Journal of Molecular Biology, 2003. 325:377-387 (abstr, web, pdf, Google Scholar)
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Finding nuclear localisation signals
M Cokol, R Nair, B Rost
EMBO Reports, 2000. 1:411-415 (abstr, web, pdf, Google Scholar)
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Protein function:
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Powerful fusion: PSI-BLAST and consensus sequences
D Przybylski, B Rost
Bioinformatics, 2008. Powerful fusion: PSI-BLAST and consensus sequences:in press (abstr, web, Google Scholar)
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Predicting protein subcellular localization using intelligent systems
R Nair, B Rost
Methods Mol Biol, 2008. 484:435-463 (abstr, web, Google Scholar)
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Predicting protein subcellular localization using intelligent systems
R Nair, B Rost
Methods Mol Biol, 2008. 484:435-463 (abstr, web, Google Scholar)
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Predicting protein subcellular localization using intelligent systems
R Nair, B Rost
Methods Mol Biol, 2008. 484:435-463 (abstr, web, Google Scholar)
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Using genetic algorithms to select most predictive protein features
A Kernytsky, B Rost
Proteins: Structure, Function, and Bioinformatics, 2008. Using genetic algorithms to select most predictive protein features:in press (abstr, web, Google Scholar)
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Using genetic algorithms to select most predictive protein features
A Kernytsky, B Rost
Proteins: Structure, Function, and Bioinformatics, 2008. Using genetic algorithms to select most predictive protein features:in press (abstr, web, Google Scholar)
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Using genetic algorithms to select most predictive protein features
A Kernytsky, B Rost
Proteins: Structure, Function, and Bioinformatics, 2008. Using genetic algorithms to select most predictive protein features:in press (abstr, web, Google Scholar)
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Using genetic algorithms to select most predictive protein features
A Kernytsky, B Rost
Proteins: Structure, Function, and Bioinformatics, 2008. Using genetic algorithms to select most predictive protein features:in press (abstr, web, Google Scholar)
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Natively unstructured loops differ from other loops
A Schlessinger, J Liu, B Rost
PLoS Computational Biology, 2007. 3:e140 (abstr, web, pdf, som, Google Scholar)
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Protein-protein interaction hot spots carved into sequences
Y Ofran, B Rost
PLoS Computational Biology, 2007. 3:e119 (abstr, web, pdf, Google Scholar)
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Protein-protein interaction hot spots carved into sequences
Y Ofran, B Rost
PLoS Computational Biology, 2007. 3:e119 (abstr, web, pdf, Google Scholar)
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Prediction of DNA-binding residues from sequence
Y Ofran, V Mysore, B Rost
Bioinformatics, 2007. 23:i347-i353 (abstr, web, pdf, Google Scholar)
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Prediction of DNA-binding residues from sequence
Y Ofran, V Mysore, B Rost
Bioinformatics, 2007. 23:i347-i353 (abstr, web, pdf, Google Scholar)
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Prediction of DNA-binding residues from sequence
Y Ofran, V Mysore, B Rost
Bioinformatics, 2007. 23:i347-i353 (abstr, web, pdf, Google Scholar)
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ISIS: Interaction Sites Identified from Sequence
Y Ofran, B Rost
Bioinformatics, 2007. 23:e13-16 (abstr, web, pdf, Google Scholar)
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ISIS: Interaction Sites Identified from Sequence
Y Ofran, B Rost
Bioinformatics, 2007. 23:e13-16 (abstr, web, pdf, Google Scholar)
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ISIS: Interaction Sites Identified from Sequence
Y Ofran, B Rost
Bioinformatics, 2007. 23:e13-16 (abstr, web, pdf, Google Scholar)
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SNAP: predict effect of non-synonymous polymorphisms on function
Y Bromberg, B Rost
Nucleic Acids Research, 2007. 35:3823-3835 (abstr, web, pdf, Google Scholar)
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SNAP: predict effect of non-synonymous polymorphisms on function
Y Bromberg, B Rost
Nucleic Acids Research, 2007. 35:3823-3835 (abstr, web, pdf, Google Scholar)
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SNAP: predict effect of non-synonymous polymorphisms on function
Y Bromberg, B Rost
Nucleic Acids Research, 2007. 35:3823-3835 (abstr, web, pdf, Google Scholar)
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PROFbval: predict flexible and rigid residues in proteins
A Schlessinger, G Yachdav, B Rost
Bioinformatics, 2006. 22:891-893 (abstr, web, pdf, som, Google Scholar)
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Epitome: Database of structure-inferred antigenic epitopes
A Schlessinger, Y Ofran, G Yachdav, B Rost
Nucleic Acids Research, 2006. 34:D777-780 (abstr, web, pdf, Google Scholar)
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Epitome: Database of structure-inferred antigenic epitopes
A Schlessinger, Y Ofran, G Yachdav, B Rost
Nucleic Acids Research, 2006. 34:D777-780 (abstr, web, pdf, Google Scholar)
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Epitome: Database of structure-inferred antigenic epitopes
A Schlessinger, Y Ofran, G Yachdav, B Rost
Nucleic Acids Research, 2006. 34:D777-780 (abstr, web, pdf, Google Scholar)
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Identifying cysteines and histidines in transition-metal-binding sites using support vector machines and neural networks
A Passerini, M Punta, A Ceroni, B Rost, P Frasconi
Proteins: Structure, Function, and Bioinformatics, 2006. 65:305-316 (pdf, Google Scholar)
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Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
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Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
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Protein–protein interactions more conserved within species than across species
S Mika, B Rost
PLoS Computational Biology, 2006. 2:e79 (abstr, web, pdf, som, Google Scholar)
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Predictive methods using protein sequence
Y Ofran, B Rost
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Predictive methods using protein sequence
Y Ofran, B Rost
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Predictive methods using protein sequence
Y Ofran, B Rost
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Beyond annotation transfer by homology: novel protein function prediction methods that can assist drug discovery
Y Ofran, M Punta, R Schneider, B Rost
Drug Discovery Today, 2005. 10:1475-1482 (pdf, Google Scholar)
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Beyond annotation transfer by homology: novel protein function prediction methods that can assist drug discovery
Y Ofran, M Punta, R Schneider, B Rost
Drug Discovery Today, 2005. 10:1475-1482 (pdf, Google Scholar)
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Beyond annotation transfer by homology: novel protein function prediction methods that can assist drug discovery
Y Ofran, M Punta, R Schneider, B Rost
Drug Discovery Today, 2005. 10:1475-1482 (pdf, Google Scholar)
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Mimicking cellular sorting improves prediction of subcellular localization
R Nair, B Rost
Journal of Molecular Biology, 2005. 348:85-100 (abstr, web, pdf, Google Scholar)
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Mimicking cellular sorting improves prediction of subcellular localization
R Nair, B Rost
Journal of Molecular Biology, 2005. 348:85-100 (abstr, web, pdf, Google Scholar)
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Mimicking cellular sorting improves prediction of subcellular localization
R Nair, B Rost
Journal of Molecular Biology, 2005. 348:85-100 (abstr, web, pdf, Google Scholar)
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NMPdb: database of nuclear matrix proteins
S Mika, B Rost
Nucleic Acids Research, 2005. 33:D160-163 (abstr, web, pdf, Google Scholar)
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NMPdb: database of nuclear matrix proteins
S Mika, B Rost
Nucleic Acids Research, 2005. 33:D160-163 (abstr, web, pdf, Google Scholar)
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LOCnet and LOCtarget: Sub-cellular localization for structural genomics targets
R Nair, B Rost
Nucleic Acids Research, 2004. 32:W517-W521 (abstr, web, pdf, Google Scholar)
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LOCnet and LOCtarget: Sub-cellular localization for structural genomics targets
R Nair, B Rost
Nucleic Acids Research, 2004. 32:W517-W521 (abstr, web, pdf, Google Scholar)
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LOCnet and LOCtarget: Sub-cellular localization for structural genomics targets
R Nair, B Rost
Nucleic Acids Research, 2004. 32:W517-W521 (abstr, web, pdf, Google Scholar)
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Annotating protein function through lexical analysis
R Nair, B Rost
AI Magazine, 2004. 25:45-56 (abstr, web, Google Scholar)
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Annotating protein function through lexical analysis
R Nair, B Rost
AI Magazine, 2004. 25:45-56 (abstr, web, Google Scholar)
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Annotating protein function through lexical analysis
R Nair, B Rost
AI Magazine, 2004. 25:45-56 (abstr, web, Google Scholar)
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Annotating protein function through lexical analysis
R Nair, B Rost
AI Magazine, 2004. 25:45-56 (abstr, web, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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Prediction of protein structure through evolution
B Rost, J Liu, D Przybylski, R Nair, H Bigelow, KO Wrzeszczynski, Y Ofran
in: 'Handbook of Chemoinformatics - from data to knowledge' (eds. J Gasteiger, T Engel), 2003. : WeinheimPrediction of protein structure through evolution:1789-1811
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Prediction of protein structure through evolution
B Rost, J Liu, D Przybylski, R Nair, H Bigelow, KO Wrzeszczynski, Y Ofran
in: 'Handbook of Chemoinformatics - from data to knowledge' (eds. J Gasteiger, T Engel), 2003. : WeinheimPrediction of protein structure through evolution:1789-1811
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Automatic prediction of protein function
B Rost, J Liu, R Nair, KO Wrzeszczynski, Y Ofran
Cellular and Molecular Life Sciences, 2003. 60:2637-2650 (abstr, web, pdf, Google Scholar)
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Predict protein-protein interaction sites from local sequence information
Y Ofran, B Rost
FEBS Letters, 2003. 544:236-239 (abstr, web, pdf, Google Scholar)
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Better prediction of sub-cellular localization by combining evolutionary and structural information
R Nair, B Rost
Proteins: Structure, Function, and Bioinformatics, 2003. 53:917-930 (abstr, web, pdf, Google Scholar)
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Better prediction of sub-cellular localization by combining evolutionary and structural information
R Nair, B Rost
Proteins: Structure, Function, and Bioinformatics, 2003. 53:917-930 (abstr, web, pdf, Google Scholar)
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DSSPcont: continuous secondary structure assignments for proteins
P Carter, CAF Andersen, B Rost
Nucleic Acids Research, 2003. 31:3293-3295 (abstr, web, pdf, Google Scholar)
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Enzyme function less conserved than anticipated
B Rost
Journal of Molecular Biology, 2002. 318:595-608 (abstr, web, pdf, Google Scholar)
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Enzyme function less conserved than anticipated
B Rost
Journal of Molecular Biology, 2002. 318:595-608 (abstr, web, pdf, Google Scholar)
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Enzyme function less conserved than anticipated
B Rost
Journal of Molecular Biology, 2002. 318:595-608 (abstr, web, pdf, Google Scholar)
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Sequence conserved for sub-cellular localization
R Nair, B Rost
Protein Science, 2002. 11:2836-2847 (abstr, web, pdf, Google Scholar)
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Sequence conserved for sub-cellular localization
R Nair, B Rost
Protein Science, 2002. 11:2836-2847 (abstr, web, pdf, Google Scholar)
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Continuum secondary structure captures protein flexibility
CAF Andersen, AG Palmer, S Brunak, B Rost
Structure, 2002. 10:175-184 (abstr, web, pdf, Google Scholar)
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Continuum secondary structure captures protein flexibility
CAF Andersen, AG Palmer, S Brunak, B Rost
Structure, 2002. 10:175-184 (abstr, web, pdf, Google Scholar)
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Continuum secondary structure captures protein flexibility
CAF Andersen, AG Palmer, S Brunak, B Rost
Structure, 2002. 10:175-184 (abstr, web, pdf, Google Scholar)
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Continuum secondary structure captures protein flexibility
CAF Andersen, AG Palmer, S Brunak, B Rost
Structure, 2002. 10:175-184 (abstr, web, pdf, Google Scholar)
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Comparing function and structure between entire proteomes
J Liu, B Rost
Protein Science, 2001. 10:1970-1979 (abstr, web, pdf, Google Scholar)
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Adaptation of protein surfaces to subcellular location
MA Andrade, SI O'Donoghue, B Rost
Journal of Molecular Biology, 1998. 276:517-525 (abstr, web, pdf, Google Scholar)
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Adaptation of protein surfaces to subcellular location
MA Andrade, SI O'Donoghue, B Rost
Journal of Molecular Biology, 1998. 276:517-525 (abstr, web, pdf, Google Scholar)
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Prediction of helical transmembrane segments at 95% accuracy
B Rost, R Casadio, P Fariselli, C Sander
Protein Science, 1995. 4:521-533 (abstr, web, pdf, Google Scholar)
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Protein structure:
abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material
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NMR structure of the peptidyl-tRNA hydrolase domain from Pseudomonas syringae expands the structural coverage of the hydrolysis domains of class 1 peptide chain release factors
KK Singarapu, R Xiao, T Acton, B Rost, GT Montelione, T Szyperski
Proteins: Structure, Function, and Bioinformatics, 2008. 71:1027-1031 (pdf, Google Scholar)
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NMR structure of the peptidyl-tRNA hydrolase domain from Pseudomonas syringae expands the structural coverage of the hydrolysis domains of class 1 peptide chain release factors
KK Singarapu, R Xiao, T Acton, B Rost, GT Montelione, T Szyperski
Proteins: Structure, Function, and Bioinformatics, 2008. 71:1027-1031 (pdf, Google Scholar)
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Prediction of protein structure in 1D – Secondary structure, membrane regions, and solvent accessibility
B Rost
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Prediction of protein structure in 1D – Secondary structure, membrane regions, and solvent accessibility
B Rost
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Prediction of protein structure in 1D – Secondary structure, membrane regions, and solvent accessibility
B Rost
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Prediction of protein structure in 1D – Secondary structure, membrane regions, and solvent accessibility
B Rost
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Predicting simplified features of protein structure
D Przybylski, B Rost
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Predicting simplified features of protein structure
D Przybylski, B Rost
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Predicting simplified features of protein structure
D Przybylski, B Rost
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Predicting simplified features of protein structure
D Przybylski, B Rost
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Predicting simplified features of protein structure
D Przybylski, B Rost
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Predicting simplified features of protein structure
D Przybylski, B Rost
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Predicting simplified features of protein structure
D Przybylski, B Rost
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Powerful fusion: PSI-BLAST and consensus sequences
D Przybylski, B Rost
Bioinformatics, 2008. Powerful fusion: PSI-BLAST and consensus sequences:in press (abstr, web, Google Scholar)
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Online tools for predicting integral membrane proteins
H Bigelow, B Rost
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Solution NMR structure of the SOS response protein YnzC from Bacillus subtilis
JM Aramini, S Sharma, YJ Huang, GV Swapna, CK Ho, K Shetty, K Cunningham, LC Ma, L Zhao, LA Owens, M Jiang, R Xiao, J Liu, MC Baran, TB Acton, B Rost, GT Montelione
Proteins: Structure, Function, and Genetics, 2008. 72:526-530 (pdf, Google Scholar)
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Solution NMR structure of the SOS response protein YnzC from Bacillus subtilis
JM Aramini, S Sharma, YJ Huang, GV Swapna, CK Ho, K Shetty, K Cunningham, LC Ma, L Zhao, LA Owens, M Jiang, R Xiao, J Liu, MC Baran, TB Acton, B Rost, GT Montelione
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Secondary structure assignment
CAF Andersen, B Rost
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Secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2008. Secondary structure assignment:in press (abstr, web, Google Scholar)
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Secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2008. Secondary structure assignment:in press (abstr, web, Google Scholar)
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Secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2008. Secondary structure assignment:in press (abstr, web, Google Scholar)
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Secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2008. Secondary structure assignment:in press (abstr, web, Google Scholar)
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Secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2008. Secondary structure assignment:in press (abstr, web, Google Scholar)
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Natively unstructured regions in proteins identified from contact predictions
A Schlessinger, M Punta, B Rost
Bioinformatics, 2007. 23:2376-2384 (abstr, web, pdf, som, Google Scholar)
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Natively unstructured regions in proteins identified from contact predictions
A Schlessinger, M Punta, B Rost
Bioinformatics, 2007. 23:2376-2384 (abstr, web, pdf, som, Google Scholar)
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Natively unstructured regions in proteins identified from contact predictions
A Schlessinger, M Punta, B Rost
Bioinformatics, 2007. 23:2376-2384 (abstr, web, pdf, som, Google Scholar)
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Natively unstructured regions in proteins identified from contact predictions
A Schlessinger, M Punta, B Rost
Bioinformatics, 2007. 23:2376-2384 (abstr, web, pdf, som, Google Scholar)
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Natively unstructured regions in proteins identified from contact predictions
A Schlessinger, M Punta, B Rost
Bioinformatics, 2007. 23:2376-2384 (abstr, web, pdf, som, Google Scholar)
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Natively unstructured loops differ from other loops
A Schlessinger, J Liu, B Rost
PLoS Computational Biology, 2007. 3:e140 (abstr, web, pdf, som, Google Scholar)
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Natively unstructured loops differ from other loops
A Schlessinger, J Liu, B Rost
PLoS Computational Biology, 2007. 3:e140 (abstr, web, pdf, som, Google Scholar)
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Natively unstructured loops differ from other loops
A Schlessinger, J Liu, B Rost
PLoS Computational Biology, 2007. 3:e140 (abstr, web, pdf, som, Google Scholar)
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Membrane protein prediction methods
M Punta, LR Forrest, H Bigelow, A Kernytsky, J Liu, B Rost
Methods, 2007. 41:460-474 (pdf, Google Scholar)
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Membrane protein prediction methods
M Punta, LR Forrest, H Bigelow, A Kernytsky, J Liu, B Rost
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Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments
D Przybylski, B Rost
Nucleic Acids Research, 2007. 35:2238-2246 (abstr, web, pdf, som, Google Scholar)
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ISIS: Interaction Sites Identified from Sequence
Y Ofran, B Rost
Bioinformatics, 2007. 23:e13-16 (abstr, web, pdf, Google Scholar)
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Novel leverage of structural genomics
J Liu, GT Montelione, B Rost
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SNAP: predict effect of non-synonymous polymorphisms on function
Y Bromberg, B Rost
Nucleic Acids Research, 2007. 35:3823-3835 (abstr, web, pdf, Google Scholar)
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SNAP: predict effect of non-synonymous polymorphisms on function
Y Bromberg, B Rost
Nucleic Acids Research, 2007. 35:3823-3835 (abstr, web, pdf, Google Scholar)
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SNAP: predict effect of non-synonymous polymorphisms on function
Y Bromberg, B Rost
Nucleic Acids Research, 2007. 35:3823-3835 (abstr, web, pdf, Google Scholar)
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Solution NMR structure of Escherichia coli ytfP expands the structural coverage of the UPF0131 protein domain family
JM Aramini, YJ Huang, GV Swapna, JR Cort, PK Rajan, R Xiao, R Shastry, TB Acton, J Liu, B Rost, MA Kennedy, GT Montelione
Proteins, 2007. 68:789-95 (pdf, Google Scholar)
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Solution NMR structure of Escherichia coli ytfP expands the structural coverage of the UPF0131 protein domain family
JM Aramini, YJ Huang, GV Swapna, JR Cort, PK Rajan, R Xiao, R Shastry, TB Acton, J Liu, B Rost, MA Kennedy, GT Montelione
Proteins, 2007. 68:789-95 (pdf, Google Scholar)
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PROFbval: predict flexible and rigid residues in proteins
A Schlessinger, G Yachdav, B Rost
Bioinformatics, 2006. 22:891-893 (abstr, web, pdf, som, Google Scholar)
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PROFbval: predict flexible and rigid residues in proteins
A Schlessinger, G Yachdav, B Rost
Bioinformatics, 2006. 22:891-893 (abstr, web, pdf, som, Google Scholar)
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PROFbval: predict flexible and rigid residues in proteins
A Schlessinger, G Yachdav, B Rost
Bioinformatics, 2006. 22:891-893 (abstr, web, pdf, som, Google Scholar)
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Epitome: Database of structure-inferred antigenic epitopes
A Schlessinger, Y Ofran, G Yachdav, B Rost
Nucleic Acids Research, 2006. 34:D777-780 (abstr, web, pdf, Google Scholar)
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Epitome: Database of structure-inferred antigenic epitopes
A Schlessinger, Y Ofran, G Yachdav, B Rost
Nucleic Acids Research, 2006. 34:D777-780 (abstr, web, pdf, Google Scholar)
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Identifying cysteines and histidines in transition-metal-binding sites using support vector machines and neural networks
A Passerini, M Punta, A Ceroni, B Rost, P Frasconi
Proteins: Structure, Function, and Bioinformatics, 2006. 65:305-316 (pdf, Google Scholar)
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Identifying cysteines and histidines in transition-metal-binding sites using support vector machines and neural networks
A Passerini, M Punta, A Ceroni, B Rost, P Frasconi
Proteins: Structure, Function, and Bioinformatics, 2006. 65:305-316 (pdf, Google Scholar)
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Outcome of a workshop on archiving structural models of biological macromolecules
HM Berman, et al.
Structure, 2006. 14:1211-1217 (pdf, Google Scholar)
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How to use protein 1D structure predicted by PROFphd
B Rost
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How to use protein 1D structure predicted by PROFphd
B Rost
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How to use protein 1D structure predicted by PROFphd
B Rost
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How to use protein 1D structure predicted by PROFphd
B Rost
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How to use protein 1D structure predicted by PROFphd
B Rost
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PROFcon: novel prediction of long-range contacts
M Punta, B Rost
Bioinformatics, 2005. 21:2960-2968 (abstr, web, pdf, Google Scholar)
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PROFcon: novel prediction of long-range contacts
M Punta, B Rost
Bioinformatics, 2005. 21:2960-2968 (abstr, web, pdf, Google Scholar)
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PROFcon: novel prediction of long-range contacts
M Punta, B Rost
Bioinformatics, 2005. 21:2960-2968 (abstr, web, pdf, Google Scholar)
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Solution structure of Archaeglobus fulgidis peptidyl-tRNA hydrolase (Pth2) provides evidence for an extensive conserved family of Pth2 enzymes in archea, bacteria, and eukaryotes
R Powers, N Mirkovic, S Goldsmith-Fischman, TB Acton, Y Chiang, YJ Huang, L Ma, PK Rajan, JR Cort, MA Kennedy, J Liu, B Rost, B Honig, D Murray, GT Montelione
Protein Science, 2005. 14:2849-61 (pdf, Google Scholar)
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Solution structure of Archaeglobus fulgidis peptidyl-tRNA hydrolase (Pth2) provides evidence for an extensive conserved family of Pth2 enzymes in archea, bacteria, and eukaryotes
R Powers, N Mirkovic, S Goldsmith-Fischman, TB Acton, Y Chiang, YJ Huang, L Ma, PK Rajan, JR Cort, MA Kennedy, J Liu, B Rost, B Honig, D Murray, GT Montelione
Protein Science, 2005. 14:2849-61 (pdf, Google Scholar)
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Solution structure of Archaeglobus fulgidis peptidyl-tRNA hydrolase (Pth2) provides evidence for an extensive conserved family of Pth2 enzymes in archea, bacteria, and eukaryotes
R Powers, N Mirkovic, S Goldsmith-Fischman, TB Acton, Y Chiang, YJ Huang, L Ma, PK Rajan, JR Cort, MA Kennedy, J Liu, B Rost, B Honig, D Murray, GT Montelione
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EVAcon: a protein contact prediction evaluation service
O Grana, VA Eyrich, F Pazos, B Rost, A Valencia
Nucleic Acids Res, 2005. 33:W347-51 (pdf, Google Scholar)
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EVAcon: a protein contact prediction evaluation service
O Grana, VA Eyrich, F Pazos, B Rost, A Valencia
Nucleic Acids Res, 2005. 33:W347-51 (pdf, Google Scholar)
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CASP6 assessment of contact prediction
O Grana, D Baker, RM Maccallum, J Meiler, M Punta, B Rost, ML Tress, A Valencia
Proteins, 2005. 61:214-224 (pdf, Google Scholar)
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CASP6 assessment of contact prediction
O Grana, D Baker, RM Maccallum, J Meiler, M Punta, B Rost, ML Tress, A Valencia
Proteins, 2005. 61:214-224 (pdf, Google Scholar)
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The protein target list of the Northeast Structural Genomics Consortium
Z Wunderlich, TB Acton, J Liu, G Kornhaber, J Everett, P Carter, N Lan, N Echols, M Gerstein, B Rost, GT Montelione
Proteins: Structure, Function, and Bioinformatics, 2004. 56:181-187 (abstr, pdf, Google Scholar)
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Improving fold recognition without folds
D Przybylski, B Rost
Journal of Molecular Biology, 2004. 341:255-269 (abstr, web, pdf, Google Scholar)
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Improving fold recognition without folds
D Przybylski, B Rost
Journal of Molecular Biology, 2004. 341:255-269 (abstr, web, pdf, Google Scholar)
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Improving fold recognition without folds
D Przybylski, B Rost
Journal of Molecular Biology, 2004. 341:255-269 (abstr, web, pdf, Google Scholar)
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1H, 13C and 15N assignments for the Archaeglobus fulgidis protein AF2095
R Powers, TB Acton, Y Chiang, PK Rajan, JR Cort, MA Kennedy, J Liu, L Ma, B Rost, GT Montelione
Journal of Biomolecular NMR, 2004. 30:107-108 (pdf, Google Scholar)
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1H, 13C and 15N assignments for the Archaeglobus fulgidis protein AF2095
R Powers, TB Acton, Y Chiang, PK Rajan, JR Cort, MA Kennedy, J Liu, L Ma, B Rost, GT Montelione
Journal of Biomolecular NMR, 2004. 30:107-108 (pdf, Google Scholar)
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1H, 13C and 15N assignments for the Archaeglobus fulgidis protein AF2095
R Powers, TB Acton, Y Chiang, PK Rajan, JR Cort, MA Kennedy, J Liu, L Ma, B Rost, GT Montelione
Journal of Biomolecular NMR, 2004. 30:107-108 (pdf, Google Scholar)
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1H, 13C and 15N assignments for the Archaeglobus fulgidis protein AF2095
R Powers, TB Acton, Y Chiang, PK Rajan, JR Cort, MA Kennedy, J Liu, L Ma, B Rost, GT Montelione
Journal of Biomolecular NMR, 2004. 30:107-108 (pdf, Google Scholar)
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1H, 13C and 15N assignments for the Archaeglobus fulgidis protein AF2095
R Powers, TB Acton, Y Chiang, PK Rajan, JR Cort, MA Kennedy, J Liu, L Ma, B Rost, GT Montelione
Journal of Biomolecular NMR, 2004. 30:107-108 (pdf, Google Scholar)
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Sequence-based prediction of protein domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:3522-3530 (abstr, web, pdf, Google Scholar)
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Sequence-based prediction of protein domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:3522-3530 (abstr, web, pdf, Google Scholar)
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CHOP: parsing proteins into structural domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:W569-W571 (abstr, web, pdf, Google Scholar)
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CHOP proteins into structural domains
J Liu, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 55:678-688 (abstr, web, pdf, Google Scholar)
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Automatic target selection for structural genomics on eukaryotes
J Liu, H Hegyi, TB Acton, GT Montelione, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 56:188-200 (abstr, web, pdf, Google Scholar)
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Rising accuracy of protein secondary structure prediction
B Rost
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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Prediction of protein structure through evolution
B Rost, J Liu, D Przybylski, R Nair, H Bigelow, KO Wrzeszczynski, Y Ofran
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Prediction of protein structure through evolution
B Rost, J Liu, D Przybylski, R Nair, H Bigelow, KO Wrzeszczynski, Y Ofran
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Prediction in 1D: secondary structure, membrane helices, and accessibility
B Rost
Methods Biochem Anal, 2003. 44:559-587 (abstr, web, Google Scholar)
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R Nair, B Rost
Proteins: Structure, Function, and Bioinformatics, 2003. 53:917-930 (abstr, web, pdf, Google Scholar)
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DSSPcont: continuous secondary structure assignments for proteins
P Carter, CAF Andersen, B Rost
Nucleic Acids Research, 2003. 31:3293-3295 (abstr, web, pdf, Google Scholar)
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Solution NMR structure of the 30S ribosomal protein S28E from Pyrococcus horikoshii
JM Aramini, YJ Huang, JR Cort, S Goldsmith-Fischman, R Xiao, LY Shih, CK Ho, J Liu, B Rost, B Honig, MA Kennedy, TB Acton, GT Montelione
Protein Science, 2003. 12:2823-2830 (abstr, pdf, Google Scholar)
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Solution NMR structure of the 30S ribosomal protein S28E from Pyrococcus horikoshii
JM Aramini, YJ Huang, JR Cort, S Goldsmith-Fischman, R Xiao, LY Shih, CK Ho, J Liu, B Rost, B Honig, MA Kennedy, TB Acton, GT Montelione
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Automatic secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2003. 44:341-363 (abstr, web, Google Scholar)
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Automatic secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2003. 44:341-363 (abstr, web, Google Scholar)
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Automatic secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2003. 44:341-363 (abstr, web, Google Scholar)
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D Przybylski, B Rost
Proteins: Structure, Function, and Bioinformatics, 2002. 46:195-205 (abstr, web, pdf, Google Scholar)
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Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles
G Pollastri, D Przybylski, B Rost, P Baldi
Proteins: Structure, Function, and Bioinformatics, 2002. 47:228-235 (pdf, Google Scholar)
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Reliability of assessment of protein structure prediction methods
MA Marti-Renom, MS Madhusudhan, A Fiser, B Rost, A Sali
Structure, 2002. 10:435-440 (pdf, Google Scholar)
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Target space for structural genomics revisited
J Liu, B Rost
Bioinformatics, 2002. 18:922-933 (abstr, web, pdf, Google Scholar)
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Continuum secondary structure captures protein flexibility
CAF Andersen, AG Palmer, S Brunak, B Rost
Structure, 2002. 10:175-184 (abstr, web, pdf, Google Scholar)
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Continuum secondary structure captures protein flexibility
CAF Andersen, AG Palmer, S Brunak, B Rost
Structure, 2002. 10:175-184 (abstr, web, pdf, Google Scholar)
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CAF Andersen, AG Palmer, S Brunak, B Rost
Structure, 2002. 10:175-184 (abstr, web, pdf, Google Scholar)
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Simple jury predicts protein secondary structure best
B Rost, P Baldi, G Barton, J Cuff, V Eyrich, D Jones, K Karplus, R King, M Ouali, G Pollastri, D Przybylski
CUBIC preprint, 2001. Simple jury predicts protein secondary structure best:5 (abstr, web, pdf, Google Scholar)
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Protein secondary structure prediction continues to rise
B Rost
Journal of Structural Biology, 2001. 134:204-218 (abstr, web, pdf, Google Scholar)
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EVA: large-scale analysis of secondary structure prediction
B Rost, V Eyrich
Proteins: Structure, Function, and Genetics, 2001. 45 Suppl 5:S192-S199 (abstr, web, pdf, Google Scholar)
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Third generation prediction of secondary structure
B Rost, C Sander
Methods in Molecular Biology, 2000. 143:71-95 (abstr, web, pdf, Google Scholar)
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A modified definition of SOV, a segment-based measure for protein secondary structure prediction assessment
A Zemla, C Venclovas, K Fidelis, B Rost
Proteins: Structure, Function, and Genetics, 1999. 34:220-223 (pdf, Google Scholar)
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Pedestrian guide to analysing sequence databases
B Rost, R Schneider
CUBIC preprint, 1999. : HeidelbergPedestrian guide to analysing sequence databases:preprint (abstr, web, Google Scholar)
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Evolution teaches neural networks
B Rost
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Protein structure prediction in 1D, 2D, and 3D
B Rost
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Protein structure prediction in 1D, 2D, and 3D
B Rost
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Marrying structure and genomics
B Rost
Structure, 1998. 6:259-263 (abstr, web, pdf, Google Scholar)
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B Rost
Structure, 1998. 6:259-263 (abstr, web, pdf, Google Scholar)
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Adaptation of protein surfaces to subcellular location
MA Andrade, SI O'Donoghue, B Rost
Journal of Molecular Biology, 1998. 276:517-525 (abstr, web, pdf, Google Scholar)
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Adaptation of protein surfaces to subcellular location
MA Andrade, SI O'Donoghue, B Rost
Journal of Molecular Biology, 1998. 276:517-525 (abstr, web, pdf, Google Scholar)
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Sisyphus and prediction of protein structure
B Rost, SI O'Donoghue
Computer Applications in Biological Science, 1997. 13:345-356 (abstr, web, pdf, Google Scholar)
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Protein structures sustain evolutionary drift
B Rost
Folding & Design, 1997. 2:S19-S24 (abstr, web, pdf, Google Scholar)
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Protein fold recognition by prediction-based threading
B Rost, R Schneider, C Sander
Journal of Molecular Biology, 1997. 270:471-480 (abstr, web, pdf, Google Scholar)
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NN which predicts protein secondary structure
B Rost
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NN which predicts protein secondary structure
B Rost
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Learning from evolution to predict protein structure
B Rost
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Learning from evolution to predict protein structure
B Rost
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Better 1D predictions by experts with machines
B Rost
Proteins: Structure, Function, and Genetics, 1997. Suppl. 1:192-197 (abstr, web, pdf, Google Scholar)
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Better 1D predictions by experts with machines
B Rost
Proteins: Structure, Function, and Genetics, 1997. Suppl. 1:192-197 (abstr, web, pdf, Google Scholar)
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Topology prediction for helical transmembrane proteins at 86% accuracy
B Rost, R Casadio, P Fariselli
Protein Science, 1996. 5:1704-1718 (abstr, web, pdf, Google Scholar)
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Topology prediction for helical transmembrane proteins at 86% accuracy
B Rost, R Casadio, P Fariselli
Protein Science, 1996. 5:1704-1718 (abstr, web, pdf, Google Scholar)
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Topology prediction for helical transmembrane proteins at 86% accuracy
B Rost, R Casadio, P Fariselli
Protein Science, 1996. 5:1704-1718 (abstr, web, pdf, Google Scholar)
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Refining neural network predictions for helical transmembrane proteins by dynamic programming
B Rost, R Casadio, P Fariselli
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PHD: predicting one-dimensional protein structure by profile based neural networks
B Rost
Methods in Enzymology, 1996. 266:525-539 (abstr, web, pdf, Google Scholar)
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PHD: predicting one-dimensional protein structure by profile based neural networks
B Rost
Methods in Enzymology, 1996. 266:525-539 (abstr, web, pdf, Google Scholar)
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PHD: predicting one-dimensional protein structure by profile based neural networks
B Rost
Methods in Enzymology, 1996. 266:525-539 (abstr, web, pdf, Google Scholar)
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PHD: predicting one-dimensional protein structure by profile based neural networks
B Rost
Methods in Enzymology, 1996. 266:525-539 (abstr, web, pdf, Google Scholar)
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Bridging the protein sequence-structure gap by structure predictions
B Rost, C Sander
Annual Review of Biophysics and Biomolecular Structure, 1996. 25:113-136 (abstr, pdf, Google Scholar)
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TOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures
B Rost
in: 'Third International Conference on Intelligent Systems for Molecular Biology' (eds. C Rawlings, D Clark, R Altman, L Hunter, T Lengauer, S Wodak), 1995. : Cambridge, EnglandTOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures:314-321 (abstr, pdf, Google Scholar)
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TOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures
B Rost
in: 'Third International Conference on Intelligent Systems for Molecular Biology' (eds. C Rawlings, D Clark, R Altman, L Hunter, T Lengauer, S Wodak), 1995. : Cambridge, EnglandTOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures:314-321 (abstr, pdf, Google Scholar)
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TOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures
B Rost
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Protein structure prediction by neural networks
B Rost, C Sander
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Protein structure prediction by neural networks
B Rost, C Sander
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Protein structure prediction by neural networks
B Rost, C Sander
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Progress of 1D protein structure prediction at last
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1995. 23:295-300 (abstr, pdf, Google Scholar)
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Progress of 1D protein structure prediction at last
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1995. 23:295-300 (abstr, pdf, Google Scholar)
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Progress of 1D protein structure prediction at last
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1995. 23:295-300 (abstr, pdf, Google Scholar)
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Prediction of helical transmembrane segments at 95% accuracy
B Rost, R Casadio, P Fariselli, C Sander
Protein Science, 1995. 4:521-533 (abstr, web, pdf, Google Scholar)
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Prediction of helical transmembrane segments at 95% accuracy
B Rost, R Casadio, P Fariselli, C Sander
Protein Science, 1995. 4:521-533 (abstr, web, pdf, Google Scholar)
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Fitting 1-D predictions into 3-D structures
B Rost
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Fitting 1-D predictions into 3-D structures
B Rost
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Structure prediction of proteins - where are we now?
B Rost, C Sander
Current Opinion in Biotechnology, 1994. 5:372-380 (abstr, pdf, Google Scholar)
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Structure prediction of proteins - where are we now?
B Rost, C Sander
Current Opinion in Biotechnology, 1994. 5:372-380 (abstr, pdf, Google Scholar)
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Redefining the goals of protein secondary structure prediction
B Rost, C Sander, R Schneider
Journal of Molecular Biology, 1994. 235:13-26 (abstr, pdf, Google Scholar)
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Redefining the goals of protein secondary structure prediction
B Rost, C Sander, R Schneider
Journal of Molecular Biology, 1994. 235:13-26 (abstr, pdf, Google Scholar)
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PHD - an automatic server for protein secondary structure prediction
B Rost, C Sander, R Schneider
CABIOS, 1994. 10:53-60 (abstr, pdf, Google Scholar)
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PHD - an automatic server for protein secondary structure prediction
B Rost, C Sander, R Schneider
CABIOS, 1994. 10:53-60 (abstr, pdf, Google Scholar)
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PHD - an automatic server for protein secondary structure prediction
B Rost, C Sander, R Schneider
CABIOS, 1994. 10:53-60 (abstr, pdf, Google Scholar)
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Evolution and neural networks - protein secondary structure prediction above 71% accuracy
B Rost, C Sander, R Schneider
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Evolution and neural networks - protein secondary structure prediction above 71% accuracy
B Rost, C Sander, R Schneider
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Evolution and neural networks - protein secondary structure prediction above 71% accuracy
B Rost, C Sander, R Schneider
in: '27th Hawaii International Conference on System Sciences' (eds. L Hunter), 1994. 5:385-394 (abstr, pdf, Google Scholar)
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Conservation and prediction of solvent accessibility in protein families
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1994. 20:216-226 (abstr, pdf, Google Scholar)
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Combining evolutionary information and neural networks to predict protein secondary structure
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1994. 19:55-72 (abstr, pdf, Google Scholar)
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Combining evolutionary information and neural networks to predict protein secondary structure
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1994. 19:55-72 (abstr, pdf, Google Scholar)
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Combining evolutionary information and neural networks to predict protein secondary structure
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1994. 19:55-72 (abstr, pdf, Google Scholar)
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Combining evolutionary information and neural networks to predict protein secondary structure
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1994. 19:55-72 (abstr, pdf, Google Scholar)
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Combining evolutionary information and neural networks to predict protein secondary structure
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1994. 19:55-72 (abstr, pdf, Google Scholar)
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Combining evolutionary information and neural networks to predict protein secondary structure
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1994. 19:55-72 (abstr, pdf, Google Scholar)
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1D secondary structure prediction through evolutionary profiles
B Rost, C Sander
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1D secondary structure prediction through evolutionary profiles
B Rost, C Sander
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1D secondary structure prediction through evolutionary profiles
B Rost, C Sander
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Data based modeling of proteins
L Holm, B Rost, C Sander, R Schneider, G Vriend
in: 'Statistical Mechanics, Protein Structure, and Protein Substrate Interactions' (eds. S Doniach), 1994. : New YorkData based modeling of proteins:277-296
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Data based modeling of proteins
L Holm, B Rost, C Sander, R Schneider, G Vriend
in: 'Statistical Mechanics, Protein Structure, and Protein Substrate Interactions' (eds. S Doniach), 1994. : New YorkData based modeling of proteins:277-296
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Secondary structure prediction of all-helical proteins in two states
B Rost, C Sander
Protein Engineering, 1993. 6:831-836 (abstr, pdf, Google Scholar)
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Secondary structure prediction of all-helical proteins in two states
B Rost, C Sander
Protein Engineering, 1993. 6:831-836 (abstr, pdf, Google Scholar)
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Secondary structure prediction of all-helical proteins in two states
B Rost, C Sander
Protein Engineering, 1993. 6:831-836 (abstr, pdf, Google Scholar)
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Progress in protein structure prediction?
B Rost, C Sander, R Schneider
Trends in Biochemical Sciences, 1993. 18:120-123 (abstr, pdf, Google Scholar)
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Progress in protein structure prediction?
B Rost, C Sander, R Schneider
Trends in Biochemical Sciences, 1993. 18:120-123 (abstr, pdf, Google Scholar)
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Prediction of protein secondary structure at better than 70% accuracy
B Rost, C Sander
Journal of Molecular Biology, 1993. 232:584-599 (abstr, web, pdf, Google Scholar)
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Prediction of protein secondary structure at better than 70% accuracy
B Rost, C Sander
Journal of Molecular Biology, 1993. 232:584-599 (abstr, web, pdf, Google Scholar)
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Prediction of protein secondary structure at better than 70% accuracy
B Rost, C Sander
Journal of Molecular Biology, 1993. 232:584-599 (abstr, web, pdf, Google Scholar)
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Neural networks in chemistry
B Rost, G Vriend
Chemical Design Autamation News, 1993. 8:24-27 (pdf, Google Scholar)
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Improved prediction of protein secondary structure by use of sequence profiles and neural networks
B Rost, C Sander
Proceedings of the National Academy of Sciences, 1993. 90:7558-7562 (abstr, pdf, Google Scholar)
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Improved prediction of protein secondary structure by use of sequence profiles and neural networks
B Rost, C Sander
Proceedings of the National Academy of Sciences, 1993. 90:7558-7562 (abstr, pdf, Google Scholar)
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Improved prediction of protein secondary structure by use of sequence profiles and neural networks
B Rost, C Sander
Proceedings of the National Academy of Sciences, 1993. 90:7558-7562 (abstr, pdf, Google Scholar)
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Molecular modelling of the Norrie disease protein predicts a cysteine knot growth factor tertiary structure
T Meitinger, A Meindl, P Bork, B Rost, C Sander, M Haasemann, J Murken
Nature Genetics, 1993. 5:376-380 (pdf, Google Scholar)
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Molecular modelling of the Norrie disease protein predicts a cysteine knot growth factor tertiary structure
T Meitinger, A Meindl, P Bork, B Rost, C Sander, M Haasemann, J Murken
Nature Genetics, 1993. 5:376-380 (pdf, Google Scholar)
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Jury returns on structure prediction
B Rost, C Sander
Nature, 1992. 360:540 (pdf, Google Scholar)
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Jury returns on structure prediction
B Rost, C Sander
Nature, 1992. 360:540 (pdf, Google Scholar)
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Exercising multi-layered networks on protein secondary structure
B Rost, C Sander
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Exercising multi-layered networks on protein secondary structure
B Rost, C Sander
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Exercising multi-layered networks on protein secondary structure
B Rost, C Sander
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Protein-protein interaction:
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Natively unstructured loops differ from other loops
A Schlessinger, J Liu, B Rost
PLoS Computational Biology, 2007. 3:e140 (abstr, web, pdf, som, Google Scholar)
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Protein-protein interaction hot spots carved into sequences
Y Ofran, B Rost
PLoS Computational Biology, 2007. 3:e119 (abstr, web, pdf, Google Scholar)
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Protein-protein interaction hot spots carved into sequences
Y Ofran, B Rost
PLoS Computational Biology, 2007. 3:e119 (abstr, web, pdf, Google Scholar)
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Protein-protein interaction hot spots carved into sequences
Y Ofran, B Rost
PLoS Computational Biology, 2007. 3:e119 (abstr, web, pdf, Google Scholar)
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Prediction of DNA-binding residues from sequence
Y Ofran, V Mysore, B Rost
Bioinformatics, 2007. 23:i347-i353 (abstr, web, pdf, Google Scholar)
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Prediction of DNA-binding residues from sequence
Y Ofran, V Mysore, B Rost
Bioinformatics, 2007. 23:i347-i353 (abstr, web, pdf, Google Scholar)
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Prediction of DNA-binding residues from sequence
Y Ofran, V Mysore, B Rost
Bioinformatics, 2007. 23:i347-i353 (abstr, web, pdf, Google Scholar)
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Prediction of DNA-binding residues from sequence
Y Ofran, V Mysore, B Rost
Bioinformatics, 2007. 23:i347-i353 (abstr, web, pdf, Google Scholar)
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Prediction of DNA-binding residues from sequence
Y Ofran, V Mysore, B Rost
Bioinformatics, 2007. 23:i347-i353 (abstr, web, pdf, Google Scholar)
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ISIS: Interaction Sites Identified from Sequence
Y Ofran, B Rost
Bioinformatics, 2007. 23:e13-16 (abstr, web, pdf, Google Scholar)
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ISIS: Interaction Sites Identified from Sequence
Y Ofran, B Rost
Bioinformatics, 2007. 23:e13-16 (abstr, web, pdf, Google Scholar)
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ISIS: Interaction Sites Identified from Sequence
Y Ofran, B Rost
Bioinformatics, 2007. 23:e13-16 (abstr, web, pdf, Google Scholar)
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ISIS: Interaction Sites Identified from Sequence
Y Ofran, B Rost
Bioinformatics, 2007. 23:e13-16 (abstr, web, pdf, Google Scholar)
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Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
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Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
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Protein–protein interactions more conserved within species than across species
S Mika, B Rost
PLoS Computational Biology, 2006. 2:e79 (abstr, web, pdf, som, Google Scholar)
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Protein–protein interactions more conserved within species than across species
S Mika, B Rost
PLoS Computational Biology, 2006. 2:e79 (abstr, web, pdf, som, Google Scholar)
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Protein–protein interactions more conserved within species than across species
S Mika, B Rost
PLoS Computational Biology, 2006. 2:e79 (abstr, web, pdf, som, Google Scholar)
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Protein–protein interactions more conserved within species than across species
S Mika, B Rost
PLoS Computational Biology, 2006. 2:e79 (abstr, web, pdf, som, Google Scholar)
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Predictive methods using protein sequence
Y Ofran, B Rost
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Predictive methods using protein sequence
Y Ofran, B Rost
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Predictive methods using protein sequence
Y Ofran, B Rost
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Beyond annotation transfer by homology: novel protein function prediction methods that can assist drug discovery
Y Ofran, M Punta, R Schneider, B Rost
Drug Discovery Today, 2005. 10:1475-1482 (pdf, Google Scholar)
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Beyond annotation transfer by homology: novel protein function prediction methods that can assist drug discovery
Y Ofran, M Punta, R Schneider, B Rost
Drug Discovery Today, 2005. 10:1475-1482 (pdf, Google Scholar)
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Beyond annotation transfer by homology: novel protein function prediction methods that can assist drug discovery
Y Ofran, M Punta, R Schneider, B Rost
Drug Discovery Today, 2005. 10:1475-1482 (pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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Predict protein-protein interaction sites from local sequence information
Y Ofran, B Rost
FEBS Letters, 2003. 544:236-239 (abstr, web, pdf, Google Scholar)
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Analysing six types of protein-protein interfaces
Y Ofran, B Rost
Journal of Molecular Biology, 2003. 325:377-387 (abstr, web, pdf, Google Scholar)
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Analysing six types of protein-protein interfaces
Y Ofran, B Rost
Journal of Molecular Biology, 2003. 325:377-387 (abstr, web, pdf, Google Scholar)
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Protemic predictions:
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Predicting simplified features of protein structure
D Przybylski, B Rost
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Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
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Protein–protein interactions more conserved within species than across species
S Mika, B Rost
PLoS Computational Biology, 2006. 2:e79 (abstr, web, pdf, som, Google Scholar)
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Distinguishing protein-coding from non-coding RNA through support vector machines
J Liu, J Gough, B Rost
PLoS Genetics, 2006. 2:e29; DOI: 10.1371/journal.pgen.0020029 (abstr, web, pdf, Google Scholar)
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How to use protein 1D structure predicted by PROFphd
B Rost
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Predictive methods using protein sequence
Y Ofran, B Rost
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Beyond annotation transfer by homology: novel protein function prediction methods that can assist drug discovery
Y Ofran, M Punta, R Schneider, B Rost
Drug Discovery Today, 2005. 10:1475-1482 (pdf, Google Scholar)
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Mimicking cellular sorting improves prediction of subcellular localization
R Nair, B Rost
Journal of Molecular Biology, 2005. 348:85-100 (abstr, web, pdf, Google Scholar)
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The transcriptional landscape of the mammalian genome
P Carninci, et al.
Science, 2005. 309:1559-1563 (pdf, Google Scholar)
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LOCnet and LOCtarget: Sub-cellular localization for structural genomics targets
R Nair, B Rost
Nucleic Acids Research, 2004. 32:W517-W521 (abstr, web, pdf, Google Scholar)
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Annotating protein function through lexical analysis
R Nair, B Rost
AI Magazine, 2004. 25:45-56 (abstr, web, Google Scholar)
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Prediction of transmembrane beta-barrels for entire proteomes
H Bigelow, D Petrey, J Liu, D Przybylski, B Rost
Nucleic Acids Research, 2004. 32:2566-2577 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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Short yeast ORFs: expressed protein or not?
B Rost
CUBIC preprint, 1999. Short yeast ORFs: expressed protein or not?: (web, Google Scholar)
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Reviews:
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Prediction of protein structure in 1D – Secondary structure, membrane regions, and solvent accessibility
B Rost
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Building a neural network for predicting protein features
M Punta, B Rost
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Predicting simplified features of protein structure
D Przybylski, B Rost
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Predicting simplified features of protein structure
D Przybylski, B Rost
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Predicting protein subcellular localization using intelligent systems
R Nair, B Rost
Methods Mol Biol, 2008. 484:435-463 (abstr, web, Google Scholar)
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Online tools for predicting integral membrane proteins
H Bigelow, B Rost
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Online tools for predicting integral membrane proteins
H Bigelow, B Rost
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Secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2008. Secondary structure assignment:in press (abstr, web, Google Scholar)
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Membrane protein prediction methods
M Punta, LR Forrest, H Bigelow, A Kernytsky, J Liu, B Rost
Methods, 2007. 41:460-474 (pdf, Google Scholar)
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How to use protein 1D structure predicted by PROFphd
B Rost
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How to use protein 1D structure predicted by PROFphd
B Rost
in: 'The Proteomics Protocols Handbook' (eds. JE Walker), 2005. : Totowa NJHow to use protein 1D structure predicted by PROFphd:875-901
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Predictive methods using protein sequence
Y Ofran, B Rost
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Predictive methods using protein sequence
Y Ofran, B Rost
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Predictive methods using protein sequence
Y Ofran, B Rost
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Beyond annotation transfer by homology: novel protein function prediction methods that can assist drug discovery
Y Ofran, M Punta, R Schneider, B Rost
Drug Discovery Today, 2005. 10:1475-1482 (pdf, Google Scholar)
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Beyond annotation transfer by homology: novel protein function prediction methods that can assist drug discovery
Y Ofran, M Punta, R Schneider, B Rost
Drug Discovery Today, 2005. 10:1475-1482 (pdf, Google Scholar)
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Annotating protein function through lexical analysis
R Nair, B Rost
AI Magazine, 2004. 25:45-56 (abstr, web, Google Scholar)
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Annotating protein function through lexical analysis
R Nair, B Rost
AI Magazine, 2004. 25:45-56 (abstr, web, Google Scholar)
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Role of transmembrane domains in the functions of Fc receptors
R Zidovetzki, B Rost, DL Armstrong, I Pecht
Journal of Biophysical Chemistry, 2003. 15:555-575 (abstr, pdf, Google Scholar)
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Rising accuracy of protein secondary structure prediction
B Rost
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Prediction of protein structure through evolution
B Rost, J Liu, D Przybylski, R Nair, H Bigelow, KO Wrzeszczynski, Y Ofran
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Prediction in 1D: secondary structure, membrane helices, and accessibility
B Rost
Methods Biochem Anal, 2003. 44:559-587 (abstr, web, Google Scholar)
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Automatic prediction of protein function
B Rost, J Liu, R Nair, KO Wrzeszczynski, Y Ofran
Cellular and Molecular Life Sciences, 2003. 60:2637-2650 (abstr, web, pdf, Google Scholar)
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Domains, motifs, and clusters in the protein universe
J Liu, B Rost
Current Opinion in Chemical Biology, 2003. 7:5-11 (abstr, web, pdf, Google Scholar)
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Automatic secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2003. 44:341-363 (abstr, web, Google Scholar)
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Did evolution leap to create the protein universe?
B Rost
Current Opinion in Structural Biology, 2002. 12:409-416 (abstr, web, pdf, Google Scholar)
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State-of-the-art in membrane prediction
CP Chen, B Rost
Applied Bioinformatics, 2002. 1:21-35 (abstr, web, pdf, Google Scholar)
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Protein secondary structure prediction continues to rise
B Rost
Journal of Structural Biology, 2001. 134:204-218 (abstr, web, pdf, Google Scholar)
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Third generation prediction of secondary structure
B Rost, C Sander
Methods in Molecular Biology, 2000. 143:71-95 (abstr, web, pdf, Google Scholar)
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Pedestrian guide to analysing sequence databases
B Rost, R Schneider
CUBIC preprint, 1999. : HeidelbergPedestrian guide to analysing sequence databases:preprint (abstr, web, Google Scholar)
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Pedestrian guide to analysing sequence databases
B Rost, R Schneider
CUBIC preprint, 1999. : HeidelbergPedestrian guide to analysing sequence databases:preprint (abstr, web, Google Scholar)
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Pedestrian guide to analysing sequence databases
B Rost, R Schneider
CUBIC preprint, 1999. : HeidelbergPedestrian guide to analysing sequence databases:preprint (abstr, web, Google Scholar)
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Evolution teaches neural networks
B Rost
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Evolution teaches neural networks
B Rost
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The role of transmembrane domains in the functions of B- and T-cell receptors
R Zidovetzki, B Rost, I Pecht
Immunology Letters, 1998. 64:97-107 (abstr, web, pdf, Google Scholar)
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Protein structure prediction in 1D, 2D, and 3D
B Rost
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Protein structure prediction in 1D, 2D, and 3D
B Rost
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Protein structure prediction in 1D, 2D, and 3D
B Rost
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Marrying structure and genomics
B Rost
Structure, 1998. 6:259-263 (abstr, web, pdf, Google Scholar)
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Sisyphus and prediction of protein structure
B Rost, SI O'Donoghue
Computer Applications in Biological Science, 1997. 13:345-356 (abstr, web, pdf, Google Scholar)
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Sisyphus and prediction of protein structure
B Rost, SI O'Donoghue
Computer Applications in Biological Science, 1997. 13:345-356 (abstr, web, pdf, Google Scholar)
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NN which predicts protein secondary structure
B Rost
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NN which predicts protein secondary structure
B Rost
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Learning from evolution to predict protein structure
B Rost
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Pitfalls of protein sequence analysis
B Rost, A Valencia
Current Opinion in Biotechnology, 1996. 7:457-461 (abstr, web, pdf, Google Scholar)
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Pitfalls of protein sequence analysis
B Rost, A Valencia
Current Opinion in Biotechnology, 1996. 7:457-461 (abstr, web, pdf, Google Scholar)
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Bridging the protein sequence-structure gap by structure predictions
B Rost, C Sander
Annual Review of Biophysics and Biomolecular Structure, 1996. 25:113-136 (abstr, pdf, Google Scholar)
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Bridging the protein sequence-structure gap by structure predictions
B Rost, C Sander
Annual Review of Biophysics and Biomolecular Structure, 1996. 25:113-136 (abstr, pdf, Google Scholar)
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Protein structure prediction by neural networks
B Rost, C Sander
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Protein structure prediction by neural networks
B Rost, C Sander
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Structure prediction of proteins - where are we now?
B Rost, C Sander
Current Opinion in Biotechnology, 1994. 5:372-380 (abstr, pdf, Google Scholar)
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Progress in protein structure prediction?
B Rost, C Sander, R Schneider
Trends in Biochemical Sciences, 1993. 18:120-123 (abstr, pdf, Google Scholar)
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Progress in protein structure prediction?
B Rost, C Sander, R Schneider
Trends in Biochemical Sciences, 1993. 18:120-123 (abstr, pdf, Google Scholar)
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Neural networks in chemistry
B Rost, G Vriend
Chemical Design Autamation News, 1993. 8:24-27 (pdf, Google Scholar)
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Secondary structure:
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Prediction of protein structure in 1D – Secondary structure, membrane regions, and solvent accessibility
B Rost
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Predicting simplified features of protein structure
D Przybylski, B Rost
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Predicting simplified features of protein structure
D Przybylski, B Rost
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Predicting simplified features of protein structure
D Przybylski, B Rost
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Secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2008. Secondary structure assignment:in press (abstr, web, Google Scholar)
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Secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2008. Secondary structure assignment:in press (abstr, web, Google Scholar)
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Natively unstructured regions in proteins identified from contact predictions
A Schlessinger, M Punta, B Rost
Bioinformatics, 2007. 23:2376-2384 (abstr, web, pdf, som, Google Scholar)
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Natively unstructured loops differ from other loops
A Schlessinger, J Liu, B Rost
PLoS Computational Biology, 2007. 3:e140 (abstr, web, pdf, som, Google Scholar)
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Membrane protein prediction methods
M Punta, LR Forrest, H Bigelow, A Kernytsky, J Liu, B Rost
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SNAP: predict effect of non-synonymous polymorphisms on function
Y Bromberg, B Rost
Nucleic Acids Research, 2007. 35:3823-3835 (abstr, web, pdf, Google Scholar)
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How to use protein 1D structure predicted by PROFphd
B Rost
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How to use protein 1D structure predicted by PROFphd
B Rost
in: 'The Proteomics Protocols Handbook' (eds. JE Walker), 2005. : Totowa NJHow to use protein 1D structure predicted by PROFphd:875-901
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How to use protein 1D structure predicted by PROFphd
B Rost
in: 'The Proteomics Protocols Handbook' (eds. JE Walker), 2005. : Totowa NJHow to use protein 1D structure predicted by PROFphd:875-901
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PROFcon: novel prediction of long-range contacts
M Punta, B Rost
Bioinformatics, 2005. 21:2960-2968 (abstr, web, pdf, Google Scholar)
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Improving fold recognition without folds
D Przybylski, B Rost
Journal of Molecular Biology, 2004. 341:255-269 (abstr, web, pdf, Google Scholar)
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Rising accuracy of protein secondary structure prediction
B Rost
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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Prediction in 1D: secondary structure, membrane helices, and accessibility
B Rost
Methods Biochem Anal, 2003. 44:559-587 (abstr, web, Google Scholar)
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Better prediction of sub-cellular localization by combining evolutionary and structural information
R Nair, B Rost
Proteins: Structure, Function, and Bioinformatics, 2003. 53:917-930 (abstr, web, pdf, Google Scholar)
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Automatic secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2003. 44:341-363 (abstr, web, Google Scholar)
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Alignments grow, secondary structure prediction improves
D Przybylski, B Rost
Proteins: Structure, Function, and Bioinformatics, 2002. 46:195-205 (abstr, web, pdf, Google Scholar)
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Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles
G Pollastri, D Przybylski, B Rost, P Baldi
Proteins: Structure, Function, and Bioinformatics, 2002. 47:228-235 (pdf, Google Scholar)
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Continuum secondary structure captures protein flexibility
CAF Andersen, AG Palmer, S Brunak, B Rost
Structure, 2002. 10:175-184 (abstr, web, pdf, Google Scholar)
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Simple jury predicts protein secondary structure best
B Rost, P Baldi, G Barton, J Cuff, V Eyrich, D Jones, K Karplus, R King, M Ouali, G Pollastri, D Przybylski
CUBIC preprint, 2001. Simple jury predicts protein secondary structure best:5 (abstr, web, pdf, Google Scholar)
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Protein secondary structure prediction continues to rise
B Rost
Journal of Structural Biology, 2001. 134:204-218 (abstr, web, pdf, Google Scholar)
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EVA: large-scale analysis of secondary structure prediction
B Rost, V Eyrich
Proteins: Structure, Function, and Genetics, 2001. 45 Suppl 5:S192-S199 (abstr, web, pdf, Google Scholar)
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Third generation prediction of secondary structure
B Rost, C Sander
Methods in Molecular Biology, 2000. 143:71-95 (abstr, web, pdf, Google Scholar)
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A modified definition of SOV, a segment-based measure for protein secondary structure prediction assessment
A Zemla, C Venclovas, K Fidelis, B Rost
Proteins: Structure, Function, and Genetics, 1999. 34:220-223 (pdf, Google Scholar)
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Pedestrian guide to analysing sequence databases
B Rost, R Schneider
CUBIC preprint, 1999. : HeidelbergPedestrian guide to analysing sequence databases:preprint (abstr, web, Google Scholar)
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Evolution teaches neural networks
B Rost
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Protein structure prediction in 1D, 2D, and 3D
B Rost
in: 'The Encyclopaedia of Computational Chemistry' (eds. PvR Schleyer, NL Allinger, T Clark, J Gasteiger, PA Kollman, HF Schaefer III, PR Schreiner), 1998. 3:2242-2255 (abstr, web, pdf, Google Scholar)
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Sisyphus and prediction of protein structure
B Rost, SI O'Donoghue
Computer Applications in Biological Science, 1997. 13:345-356 (abstr, web, pdf, Google Scholar)
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NN which predicts protein secondary structure
B Rost
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NN which predicts protein secondary structure
B Rost
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Learning from evolution to predict protein structure
B Rost
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Better 1D predictions by experts with machines
B Rost
Proteins: Structure, Function, and Genetics, 1997. Suppl. 1:192-197 (abstr, web, pdf, Google Scholar)
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Better 1D predictions by experts with machines
B Rost
Proteins: Structure, Function, and Genetics, 1997. Suppl. 1:192-197 (abstr, web, pdf, Google Scholar)
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Topology prediction for helical transmembrane proteins at 86% accuracy
B Rost, R Casadio, P Fariselli
Protein Science, 1996. 5:1704-1718 (abstr, web, pdf, Google Scholar)
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Topology prediction for helical transmembrane proteins at 86% accuracy
B Rost, R Casadio, P Fariselli
Protein Science, 1996. 5:1704-1718 (abstr, web, pdf, Google Scholar)
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PHD: predicting one-dimensional protein structure by profile based neural networks
B Rost
Methods in Enzymology, 1996. 266:525-539 (abstr, web, pdf, Google Scholar)
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PHD: predicting one-dimensional protein structure by profile based neural networks
B Rost
Methods in Enzymology, 1996. 266:525-539 (abstr, web, pdf, Google Scholar)
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PHD: predicting one-dimensional protein structure by profile based neural networks
B Rost
Methods in Enzymology, 1996. 266:525-539 (abstr, web, pdf, Google Scholar)
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Bridging the protein sequence-structure gap by structure predictions
B Rost, C Sander
Annual Review of Biophysics and Biomolecular Structure, 1996. 25:113-136 (abstr, pdf, Google Scholar)
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TOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures
B Rost
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TOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures
B Rost
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Protein structure prediction by neural networks
B Rost, C Sander
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Protein structure prediction by neural networks
B Rost, C Sander
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Progress of 1D protein structure prediction at last
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1995. 23:295-300 (abstr, pdf, Google Scholar)
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Progress of 1D protein structure prediction at last
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1995. 23:295-300 (abstr, pdf, Google Scholar)
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Fitting 1-D predictions into 3-D structures
B Rost
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Structure prediction of proteins - where are we now?
B Rost, C Sander
Current Opinion in Biotechnology, 1994. 5:372-380 (abstr, pdf, Google Scholar)
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Redefining the goals of protein secondary structure prediction
B Rost, C Sander, R Schneider
Journal of Molecular Biology, 1994. 235:13-26 (abstr, pdf, Google Scholar)
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PHD - an automatic server for protein secondary structure prediction
B Rost, C Sander, R Schneider
CABIOS, 1994. 10:53-60 (abstr, pdf, Google Scholar)
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PHD - an automatic server for protein secondary structure prediction
B Rost, C Sander, R Schneider
CABIOS, 1994. 10:53-60 (abstr, pdf, Google Scholar)
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Evolution and neural networks - protein secondary structure prediction above 71% accuracy
B Rost, C Sander, R Schneider
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Evolution and neural networks - protein secondary structure prediction above 71% accuracy
B Rost, C Sander, R Schneider
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Combining evolutionary information and neural networks to predict protein secondary structure
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1994. 19:55-72 (abstr, pdf, Google Scholar)
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Combining evolutionary information and neural networks to predict protein secondary structure
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1994. 19:55-72 (abstr, pdf, Google Scholar)
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Combining evolutionary information and neural networks to predict protein secondary structure
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1994. 19:55-72 (abstr, pdf, Google Scholar)
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Combining evolutionary information and neural networks to predict protein secondary structure
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1994. 19:55-72 (abstr, pdf, Google Scholar)
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Combining evolutionary information and neural networks to predict protein secondary structure
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1994. 19:55-72 (abstr, pdf, Google Scholar)
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1D secondary structure prediction through evolutionary profiles
B Rost, C Sander
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1D secondary structure prediction through evolutionary profiles
B Rost, C Sander
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Secondary structure prediction of all-helical proteins in two states
B Rost, C Sander
Protein Engineering, 1993. 6:831-836 (abstr, pdf, Google Scholar)
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Secondary structure prediction of all-helical proteins in two states
B Rost, C Sander
Protein Engineering, 1993. 6:831-836 (abstr, pdf, Google Scholar)
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Progress in protein structure prediction?
B Rost, C Sander, R Schneider
Trends in Biochemical Sciences, 1993. 18:120-123 (abstr, pdf, Google Scholar)
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Prediction of protein secondary structure at better than 70% accuracy
B Rost, C Sander
Journal of Molecular Biology, 1993. 232:584-599 (abstr, web, pdf, Google Scholar)
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Prediction of protein secondary structure at better than 70% accuracy
B Rost, C Sander
Journal of Molecular Biology, 1993. 232:584-599 (abstr, web, pdf, Google Scholar)
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Improved prediction of protein secondary structure by use of sequence profiles and neural networks
B Rost, C Sander
Proceedings of the National Academy of Sciences, 1993. 90:7558-7562 (abstr, pdf, Google Scholar)
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Improved prediction of protein secondary structure by use of sequence profiles and neural networks
B Rost, C Sander
Proceedings of the National Academy of Sciences, 1993. 90:7558-7562 (abstr, pdf, Google Scholar)
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Jury returns on structure prediction
B Rost, C Sander
Nature, 1992. 360:540 (pdf, Google Scholar)
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Exercising multi-layered networks on protein secondary structure
B Rost, C Sander
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Exercising multi-layered networks on protein secondary structure
B Rost, C Sander
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Sequence analysis:
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Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments
D Przybylski, B Rost
Nucleic Acids Research, 2007. 35:2238-2246 (abstr, web, pdf, som, Google Scholar)
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ISIS: Interaction Sites Identified from Sequence
Y Ofran, B Rost
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SNAP: predict effect of non-synonymous polymorphisms on function
Y Bromberg, B Rost
Nucleic Acids Research, 2007. 35:3823-3835 (abstr, web, pdf, Google Scholar)
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PROFbval: predict flexible and rigid residues in proteins
A Schlessinger, G Yachdav, B Rost
Bioinformatics, 2006. 22:891-893 (abstr, web, pdf, som, Google Scholar)
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Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006. 22:e402-7 (abstr, web, pdf, Google Scholar)
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How to use protein 1D structure predicted by PROFphd
B Rost
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Predictive methods using protein sequence
Y Ofran, B Rost
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Beyond annotation transfer by homology: novel protein function prediction methods that can assist drug discovery
Y Ofran, M Punta, R Schneider, B Rost
Drug Discovery Today, 2005. 10:1475-1482 (pdf, Google Scholar)
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The transcriptional landscape of the mammalian genome
P Carninci, et al.
Science, 2005. 309:1559-1563 (pdf, Google Scholar)
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The transcriptional landscape of the mammalian genome
P Carninci, et al.
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Improving fold recognition without folds
D Przybylski, B Rost
Journal of Molecular Biology, 2004. 341:255-269 (abstr, web, pdf, Google Scholar)
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Sequence-based prediction of protein domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:3522-3530 (abstr, web, pdf, Google Scholar)
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Sequence-based prediction of protein domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:3522-3530 (abstr, web, pdf, Google Scholar)
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CHOP: parsing proteins into structural domains
J Liu, B Rost
Nucleic Acids Research, 2004. 32:W569-W571 (abstr, web, pdf, Google Scholar)
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CHOP proteins into structural domains
J Liu, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 55:678-688 (abstr, web, pdf, Google Scholar)
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Automatic target selection for structural genomics on eukaryotes
J Liu, H Hegyi, TB Acton, GT Montelione, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004. 56:188-200 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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Target space for structural genomics revisited
J Liu, B Rost
Bioinformatics, 2002. 18:922-933 (abstr, web, pdf, Google Scholar)
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Twilight zone of protein sequence alignments
B Rost
Protein Engineering, 1999. 12:85-94 (abstr, web, pdf, Google Scholar)
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Short yeast ORFs: expressed protein or not?
B Rost
CUBIC preprint, 1999. Short yeast ORFs: expressed protein or not?: (web, Google Scholar)
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Pedestrian guide to analysing sequence databases
B Rost, R Schneider
CUBIC preprint, 1999. : HeidelbergPedestrian guide to analysing sequence databases:preprint (abstr, web, Google Scholar)
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A platform for integrating threading results with protein family analyses
F Pazos, B Rost, A Valencia
Bioinformatics, 1999. 15:1062-1063 (abstr, web, pdf, Google Scholar)
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Marrying structure and genomics
B Rost
Structure, 1998. 6:259-263 (abstr, web, pdf, Google Scholar)
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B Rost, A Valencia
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Fitting 1-D predictions into 3-D structures
B Rost
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Solvent accessibility:
abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material
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SNAP: predict effect of non-synonymous polymorphisms on function
Y Bromberg, B Rost
Nucleic Acids Research, 2007. 35:3823-3835 (abstr, web, pdf, Google Scholar)
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How to use protein 1D structure predicted by PROFphd
B Rost
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PROFcon: novel prediction of long-range contacts
M Punta, B Rost
Bioinformatics, 2005. 21:2960-2968 (abstr, web, pdf, Google Scholar)
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D Przybylski, B Rost
Journal of Molecular Biology, 2004. 341:255-269 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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Adaptation of protein surfaces to subcellular location
MA Andrade, SI O'Donoghue, B Rost
Journal of Molecular Biology, 1998. 276:517-525 (abstr, web, pdf, Google Scholar)
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Learning from evolution to predict protein structure
B Rost
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Better 1D predictions by experts with machines
B Rost
Proteins: Structure, Function, and Genetics, 1997. Suppl. 1:192-197 (abstr, web, pdf, Google Scholar)
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PHD: predicting one-dimensional protein structure by profile based neural networks
B Rost
Methods in Enzymology, 1996. 266:525-539 (abstr, web, pdf, Google Scholar)
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PHD: predicting one-dimensional protein structure by profile based neural networks
B Rost
Methods in Enzymology, 1996. 266:525-539 (abstr, web, pdf, Google Scholar)
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TOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures
B Rost
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Progress of 1D protein structure prediction at last
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1995. 23:295-300 (abstr, pdf, Google Scholar)
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Fitting 1-D predictions into 3-D structures
B Rost
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Conservation and prediction of solvent accessibility in protein families
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1994. 20:216-226 (abstr, pdf, Google Scholar)
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Conservation and prediction of solvent accessibility in protein families
B Rost, C Sander
Proteins: Structure, Function, and Genetics, 1994. 20:216-226 (abstr, pdf, Google Scholar)
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Structural genomics:
abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material
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NMR structure of the peptidyl-tRNA hydrolase domain from Pseudomonas syringae expands the structural coverage of the hydrolysis domains of class 1 peptide chain release factors
KK Singarapu, R Xiao, T Acton, B Rost, GT Montelione, T Szyperski
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Solution NMR structure of the SOS response protein YnzC from Bacillus subtilis
JM Aramini, S Sharma, YJ Huang, GV Swapna, CK Ho, K Shetty, K Cunningham, LC Ma, L Zhao, LA Owens, M Jiang, R Xiao, J Liu, MC Baran, TB Acton, B Rost, GT Montelione
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Secondary structure assignment
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Solution structure of Archaeglobus fulgidis peptidyl-tRNA hydrolase (Pth2) provides evidence for an extensive conserved family of Pth2 enzymes in archea, bacteria, and eukaryotes
R Powers, N Mirkovic, S Goldsmith-Fischman, TB Acton, Y Chiang, YJ Huang, L Ma, PK Rajan, JR Cort, MA Kennedy, J Liu, B Rost, B Honig, D Murray, GT Montelione
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The protein target list of the Northeast Structural Genomics Consortium
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1H, 13C and 15N assignments for the Archaeglobus fulgidis protein AF2095
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LOCnet and LOCtarget: Sub-cellular localization for structural genomics targets
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Nucleic Acids Research, 2004. 32:W517-W521 (abstr, web, pdf, Google Scholar)
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R Nair, B Rost
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Nucleic Acids Research, 2004. 32:3522-3530 (abstr, web, pdf, Google Scholar)
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Nucleic Acids Research, 2004. 32:W569-W571 (abstr, web, pdf, Google Scholar)
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Proteins: Structure, Function, and Bioinformatics, 2004. 55:678-688 (abstr, web, pdf, Google Scholar)
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Automatic target selection for structural genomics on eukaryotes
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Proteins: Structure, Function, and Bioinformatics, 2004. 56:188-200 (abstr, web, pdf, Google Scholar)
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Automatic target selection for structural genomics on eukaryotes
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Prediction of transmembrane beta-barrels for entire proteomes
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Nucleic Acids Research, 2004. 32:2566-2577 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
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B Rost
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Automatic secondary structure assignment
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Subcellular localization:
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R Nair, B Rost
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R Nair, B Rost
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Using genetic algorithms to select most predictive protein features
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Create and assess protein networks through molecular characteristics of individual proteins
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Create and assess protein networks through molecular characteristics of individual proteins
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Predictive methods using protein sequence
Y Ofran, B Rost
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Beyond annotation transfer by homology: novel protein function prediction methods that can assist drug discovery
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Drug Discovery Today, 2005. 10:1475-1482 (pdf, Google Scholar)
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Mimicking cellular sorting improves prediction of subcellular localization
R Nair, B Rost
Journal of Molecular Biology, 2005. 348:85-100 (abstr, web, pdf, Google Scholar)
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R Nair, B Rost
Journal of Molecular Biology, 2005. 348:85-100 (abstr, web, pdf, Google Scholar)
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R Nair, B Rost
Journal of Molecular Biology, 2005. 348:85-100 (abstr, web, pdf, Google Scholar)
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R Nair, B Rost
Journal of Molecular Biology, 2005. 348:85-100 (abstr, web, pdf, Google Scholar)
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NMPdb: database of nuclear matrix proteins
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Nucleic Acids Research, 2005. 33:D160-163 (abstr, web, pdf, Google Scholar)
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NMPdb: database of nuclear matrix proteins
S Mika, B Rost
Nucleic Acids Research, 2005. 33:D160-163 (abstr, web, pdf, Google Scholar)
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NMPdb: database of nuclear matrix proteins
S Mika, B Rost
Nucleic Acids Research, 2005. 33:D160-163 (abstr, web, pdf, Google Scholar)
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LOCnet and LOCtarget: Sub-cellular localization for structural genomics targets
R Nair, B Rost
Nucleic Acids Research, 2004. 32:W517-W521 (abstr, web, pdf, Google Scholar)
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LOCnet and LOCtarget: Sub-cellular localization for structural genomics targets
R Nair, B Rost
Nucleic Acids Research, 2004. 32:W517-W521 (abstr, web, pdf, Google Scholar)
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LOCnet and LOCtarget: Sub-cellular localization for structural genomics targets
R Nair, B Rost
Nucleic Acids Research, 2004. 32:W517-W521 (abstr, web, pdf, Google Scholar)
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LOCnet and LOCtarget: Sub-cellular localization for structural genomics targets
R Nair, B Rost
Nucleic Acids Research, 2004. 32:W517-W521 (abstr, web, pdf, Google Scholar)
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LOCnet and LOCtarget: Sub-cellular localization for structural genomics targets
R Nair, B Rost
Nucleic Acids Research, 2004. 32:W517-W521 (abstr, web, pdf, Google Scholar)
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Annotating protein function through lexical analysis
R Nair, B Rost
AI Magazine, 2004. 25:45-56 (abstr, web, Google Scholar)
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Annotating protein function through lexical analysis
R Nair, B Rost
AI Magazine, 2004. 25:45-56 (abstr, web, Google Scholar)
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Annotating protein function through lexical analysis
R Nair, B Rost
AI Magazine, 2004. 25:45-56 (abstr, web, Google Scholar)
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Annotating protein function through lexical analysis
R Nair, B Rost
AI Magazine, 2004. 25:45-56 (abstr, web, Google Scholar)
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Annotating protein function through lexical analysis
R Nair, B Rost
AI Magazine, 2004. 25:45-56 (abstr, web, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003. 31:3300-3304 (abstr, web, pdf, Google Scholar)
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Automatic prediction of protein function
B Rost, J Liu, R Nair, KO Wrzeszczynski, Y Ofran
Cellular and Molecular Life Sciences, 2003. 60:2637-2650 (abstr, web, pdf, Google Scholar)
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Automatic prediction of protein function
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Cellular and Molecular Life Sciences, 2003. 60:2637-2650 (abstr, web, pdf, Google Scholar)
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Better prediction of sub-cellular localization by combining evolutionary and structural information
R Nair, B Rost
Proteins: Structure, Function, and Bioinformatics, 2003. 53:917-930 (abstr, web, pdf, Google Scholar)
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Better prediction of sub-cellular localization by combining evolutionary and structural information
R Nair, B Rost
Proteins: Structure, Function, and Bioinformatics, 2003. 53:917-930 (abstr, web, pdf, Google Scholar)
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Better prediction of sub-cellular localization by combining evolutionary and structural information
R Nair, B Rost
Proteins: Structure, Function, and Bioinformatics, 2003. 53:917-930 (abstr, web, pdf, Google Scholar)
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Better prediction of sub-cellular localization by combining evolutionary and structural information
R Nair, B Rost
Proteins: Structure, Function, and Bioinformatics, 2003. 53:917-930 (abstr, web, pdf, Google Scholar)
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Sequence conserved for sub-cellular localization
R Nair, B Rost
Protein Science, 2002. 11:2836-2847 (abstr, web, pdf, Google Scholar)
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Inferring sub-cellular localisation through automated lexical analysis
R Nair, B Rost
Bioinformatics, 2002. 18:S78-S86 (abstr, web, pdf, Google Scholar)
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Finding nuclear localisation signals
M Cokol, R Nair, B Rost
EMBO Reports, 2000. 1:411-415 (abstr, web, pdf, Google Scholar)
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Finding nuclear localisation signals
M Cokol, R Nair, B Rost
EMBO Reports, 2000. 1:411-415 (abstr, web, pdf, Google Scholar)
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Adaptation of protein surfaces to subcellular location
MA Andrade, SI O'Donoghue, B Rost
Journal of Molecular Biology, 1998. 276:517-525 (abstr, web, pdf, Google Scholar)
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Threading (remote homology):
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Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments
D Przybylski, B Rost
Nucleic Acids Research, 2007. 35:2238-2246 (abstr, web, pdf, som, Google Scholar)
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Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments
D Przybylski, B Rost
Nucleic Acids Research, 2007. 35:2238-2246 (abstr, web, pdf, som, Google Scholar)
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Improving fold recognition without folds
D Przybylski, B Rost
Journal of Molecular Biology, 2004. 341:255-269 (abstr, web, pdf, Google Scholar)
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Improving fold recognition without folds
D Przybylski, B Rost
Journal of Molecular Biology, 2004. 341:255-269 (abstr, web, pdf, Google Scholar)
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A platform for integrating threading results with protein family analyses
F Pazos, B Rost, A Valencia
Bioinformatics, 1999. 15:1062-1063 (abstr, web, pdf, Google Scholar)
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Effective use of sequence correlation and conservation in fold recognition
O Olmea, B Rost, A Valencia
Journal of Molecular Biology, 1999. 293:1221-1239 (abstr, web, pdf, Google Scholar)
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Protein fold recognition by prediction-based threading
B Rost, R Schneider, C Sander
Journal of Molecular Biology, 1997. 270:471-480 (abstr, web, pdf, Google Scholar)
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Update on protein structure prediction: results of the 1995 IRBM workshop
T Hubbard, et al.
Folding & Design, 1996. 1:R55-R63 (abstr, pdf, Google Scholar)
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TOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures
B Rost
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TOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures
B Rost
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Fitting 1-D predictions into 3-D structures
B Rost
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Fitting 1-D predictions into 3-D structures
B Rost
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