Problem Based Learning SoSe 2022/WiSe 2022/23

Type 5S (1 year module, 2  SWS/Summer, 3 SWS/Winter)
Lecturer Burkhard Rost, Kyra Erckert, Tobias Senoner
Time Monday, 12:00 - 14:00
Room MI 01.09.034 / online
Language English

News (newest info always at the top)

PBL will most likely happen in person in Garching (depending on COVID-19 regulations and development).

Detailed information regarding the course will be sent to all students registered for PBL2022/23 via e-mail.

More detailed descriptions of the topics will be published in the week before the kickoff meeting. 4-5 students will work on the same topic, but will focus on different aspects of the given problem.

Kick-off meeting

The kick-off meeting will take place online on Monday, 25.04.2022, 12:00-14:00. Attendance is mandatory. All important information including detailed description of the topcis will be sent out via e-mail to all registered participants by April 18th.


Date   Students Supervisors
25.04.2022 Kick-off Meeting Everyone Everyone
02.05.2022 How to give a good presentation (Lecture Talk) Everyone Erckert, Senoner
30.05.2022 Introduction Talks TBA TBA
13.06.2022 Introduction Talks TBA TBA
20.06.2022 Introduction to Python and Version Management (Lecture Talk) Everyone Nechaev, Erckert, Senoner
27.06.2022 Introduction to Machine Learning & scikit-learn (Lecture Talk) Everyone Erckert, Senoner
04.07.2022 Advanced Machine Learning with Pytorch (Lecture Talk) Everyone Erckert, Senoner
18.07.2022 First Milestone Talks Everyone Everyone



Course outline and goals

This course focuses on the application of machine learning to predict various aspects of protein function and structure. During this course, independent of the assigned prediction task, students are going to:

  • perform literature research of a pre-defined topic
  • get a general understanding of machine learning and how to apply machine learning to biological data
  • develop and correctly evaluate a machine learning model including parameter optimization (using Python 3)
  • present milestones and final results in various presentations to the other students and supervisors
  • summarize results in a paper-like scientific report at the end of the course

Students will work in groups. They will present their topic and biological background as well as their dataset together. They will work on the same prediction task, but will follow different approaches as discussed with their supervisor. In the end, each group will merge their result and will present the results and a final conclusion in a talk and a written scientific report.


  • Transmembrane Proteins (Supervisor: Céline Marquet)
  • Signal Peptides (Supervisor: Dmitrii Nechaev)
  • Enzymatic Activity (Supervisor: Tobias Senoner)
  • Protein Domain Classification (Supervisor: Tobias Olenyi)


For your final report, please use the Bioinformatics template

Use a numbered citation style listing references in the order they appear in the text, i.e. the first reference in the text has number 1. Citations in the text should appear as (1) or [1]. Don't use footnotes but include the references at the end of the text.