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263-5351-00L 6 Credits MSC , WBZ D-BSSE , D-INFK , D-MATH , D-ITET
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Machine Learning for Genomics

Lecturers & Examiners: Prof. Dr. Valentina Boeva
VVZ CR 3.8

Last Updated: 2026-06-01 11:30:47

Abstract

The course reviews solutions provided by machine learning to the most challenging questions in human genomics.

Objective

Over the last few years, the parallel development of machine learning methods and molecular profiling technologies for human cells, such as sequencing, created an extremely powerful tool to get insights into the cellular mechanisms in healthy and diseased contexts. In this course, we will discuss the state-of-the-art machine learning methodology solving or attempting to solve common problems in human genomics. At the end of the course, you will be familiar with (1) classical and advanced machine learning architectures used in genomics, (2) bioinformatics analysis of human genomic and transcriptomic data, and (3) data types used in this field.

Content

- Short introduction to major concepts of molecular biology: DNA, genes, genome, central dogma, transcription factors, epigenetic code, DNA methylation, signaling pathways - Prediction of transcription factor binding sites, open chromatin, histone marks, promoters, nucleosome positioning (convolutional neural networks, position weight matrices) - Prediction of variant effects and gene expression (hidden Markov models, topic models) - Deconvolution of mixed signal (NMF, ICA) - DNA, RNA and protein folding (RNN, LSTM, transformers) - Data imputation for single-cell RNA-seq data, clustering and annotation (methods on graphs) - Batch correction (autoencoders) - Methods for the spatial molecular data (optimal transport) - Survival analysis (Cox proportional hazard model, regularization penalties, multi-omics)

Resources

Learning Materials (Links)

General Information

Language
English
Levels
MSC , WBZ
Frequency
Yearly recurring

Examination

Type
end-of-semester examination
Mode
written 180 minutes
Aids
None
Digital
The exam takes place on devices provided by ETH Zurich.
The exam might take place on a computer.Besides the end-of-semester exam, there will be two course projects that can be done in groups. As a compulsory continuous performance assessment task, the projects must be passed on their own and have a bonus/penalty function. The practical projects are an integral part (60 hours of work, 2 credits) of the course. Participation is mandatory.Failing the project results in a failing grade for the overall examination of Machine Learning for Genomics. Students who do not pass the project are required to de-register from the exam and will otherwise be treated as a no-show. Project work (1 project out of the two) can be replaced by a paper presentation (limited number of spots available).Attending paper presentations is considered to be a mandatory part of the course; students who fail to attend at least 9 paper presentations fail the course.

Registration & Places

Max Places
100
Priority: Registration for the course unit is until 28.09.2025 only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture Machine Learning for Genomics
  • Wed 12:15-14:00 (ML F 39)
2 h weekly
exercise Machine Learning for Genomics
  • Wed 14:15-16:00 (ML F 39)
2 h weekly
independent project Machine Learning for Genomics No time listed 1 h weekly

Offered In