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
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