263-5351-00L  Machine Learning for Genomics

SemesterFrühjahrssemester 2022
DozierendeV. Boeva
Periodizitätjährlich wiederkehrende Veranstaltung
KommentarThe 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 Std.
Mi12:15-14:00ML E 12 »
V. Boeva
263-5351-00 UMachine Learning for Genomics1 Std.
Mi16:15-17:00CAB G 59 »
V. Boeva
263-5351-00 AMachine Learning for Genomics1 Std.V. Boeva


KurzbeschreibungThe course reviews solutions that machine learning provides to the most challenging questions in human genomics.
LernzielOver 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.
Inhalt- 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)
Voraussetzungen / BesonderesIntroduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line; having taken Computational Biomedicine is highly recommended


Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte5 KP
PrüfendeV. Boeva
RepetitionEs wird ein Repetitionstermin in den ersten zwei Wochen des unmittelbar nachfolgenden Semesters angeboten.
Prüfungsmodusschriftlich 180 Minuten
Zusatzinformation zum PrüfungsmodusThe 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).
Hilfsmittel schriftlichKeine
Online-PrüfungDie Prüfung kann am Computer stattfinden.
FernprüfungDas Ablegen als Fernprüfung ist nicht möglich.


Es werden nur die öffentlichen Lernmaterialien aufgeführt.


Keine Informationen zu Gruppen vorhanden.


PlätzeMaximal 75
WartelisteBis 06.03.2022

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