This website is no longer updated.

As of 1.10.2022, the Faculty of Physics has been merged into the TUM School of Natural Sciences with the website For more information read Conversion of Websites.

de | en

Exercise Machine Learning for Regulatory Genomics (IN2393)

Course 0000003251 in SS 2023

General Data

Course Type exercise
Semester Weekly Hours 2 SWS
Organisational Unit Informatics 29 - Chair of Computational Molecular Medicine (Prof. Gagneur)
Lecturers Julien Gagneur
Matthias Heinig
Dates Tue, 15:30–17:00, MI 00.08.038

Assignment to Modules

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks This is a two-part module: (1) Six lectures introduce biological mechanisms, experimental assays, and computational models for regulatory genomics. The six lectures are supported with modeling exercises in python. This is followed by (2) an eight-week hands-on project. The lectures are organized around steps of gene expression: ● Introduction to gene regulation and sequence-based computational models of gene regulation ● Transcriptional regulation ● Chromatin-mediated regulation ● RNA splicing ● RNA modification and degradation ● Translation Over these lectures, computational methods are introduced including: ● Fitting procedures of deep neural network ● Convolutional Neural Networks ● LSTM and transformers ● Embeddings for sequence data ● Multi-task learning and transfer learning ● End-to-end learning ● Analytical and visualisation techniques for model interpretation
Links TUMonline entry

Equivalent Courses (e. g. in other semesters)

Top of page