263-5351-00L  Machine Learning for Genomics

SemesterSpring Semester 2022
LecturersV. Boeva
Periodicityyearly recurring course
Language of instructionEnglish
CommentThe deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not provide project work and/or do not show up for the exam, will officially fail the course.

Number of participants limited to 75.


263-5351-00 VMachine Learning for Genomics2 hrs
Wed12:15-14:00ML E 12 »
V. Boeva
263-5351-00 UMachine Learning for Genomics1 hrs
Wed16:15-17:00CAB G 59 »
V. Boeva
263-5351-00 AMachine Learning for Genomics1 hrsV. Boeva

Catalogue data

AbstractThe course reviews solutions that machine learning provides to the most challenging questions in human genomics.
ObjectiveOver 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
- DNA, RNA and protein folding (RNN, LSTM, transformers)
- Data imputation for single cell RNA-seq data, clustering and annotation (diffusion and methods on graphs)
- Batch correction (autoencoders, optimal transport)
- Survival analysis (Cox proportional hazard model, regularization penalties, multi-omics, multi-tasking)
Prerequisites / NoticeIntroduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line; having taken Computational Biomedicine is highly recommended

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits5 credits
ExaminersV. Boeva
Typeend-of-semester examination
Language of examinationEnglish
RepetitionA repetition date will be offered in the first two weeks of the semester immediately consecutive.
Mode of examinationwritten 180 minutes
Additional information on mode of examinationThe exam might take place at a computer.

Beside 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).
Written aidsNone
Online examinationThe examination may take place on the computer.
Distance examinationIt is not possible to take a distance examination.

Learning materials

Main linkInformation
Only public learning materials are listed.


No information on groups available.


Places75 at the most
Waiting listuntil 06.03.2022

Offered in

CAS in Computer ScienceFocus Courses and ElectivesWInformation
Computational Biology and Bioinformatics MasterTheoryWInformation
Cyber Security MasterElective CoursesWInformation
Data Science MasterInterdisciplinary ElectivesWInformation
Computer Science MasterElective CoursesWInformation
Computer Science MasterMinor in Machine LearningWInformation
Statistics MasterSubject Specific ElectivesWInformation