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Lecture Machine Learning for Regulatory Genomics (IN2393)

Course 0000003239 in SS 2021

General Data

Course Type lecture
Semester Weekly Hours 2 SWS
Organisational Unit Informatics 29 - Chair of Computational Molecular Medicine (Prof. Gagneur)
Lecturers Julien Gagneur
Matthias Heinig
Dates Tue, 14:00–15:30, virtuell

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 Gene expression refers to how cells read the information encoded in genomes. At the end of the module students are able to: 1. Describe major steps of gene expression from accessing DNA to determining protein abundance. 2. Describe genome-wide assays employed to assess various steps of gene expression 3. Describe the concept of massively parallel reporter assays 4. Describe and apply deep learning methods to perform sequence-based predictions 5. Describe and apply the concept of model interpretation 6. Describe and apply the concept of convolutional neural network 7. Describe and apply the concept of transformers 8. Apply deep learning for sequence-based modeling of a genome-wide assay. Evaluate model performance and provide biological interpretation of its application to real data.
Links E-Learning course (e. g. Moodle)
TUMonline entry
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