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ergänzende Hinweise |
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-Eintrag
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