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261-5100-00L 5 Credits MSC D-ITET , D-MATH , D-INFK

Computational Biomedicine

Lecturers & Examiners: Prof. Dr. Gunnar Rätsch, Prof. Dr. Valentina Boeva
Number of participants limited to 120.
VVZ CR n/a

Last Updated: 2026-02-05 15:48:23

Abstract

The course critically reviews central problems in Biomedicine and discusses the technical foundations and solutions for these problems.

Objective

Over the past years, rapid technological advancements have transformed classical disciplines such as biology and medicine into fields of apllied data science. While the sheer amount of the collected data often makes computational approaches inevitable for analysis, it is the domain specific structure and close relation to research and clinic, that call for accurate, robust and efficient algorithms. In this course we will critically review central problems in Biomedicine and will discuss the technical foundations and solutions for these problems.

Content

The course will consist of three topic clusters that will cover different aspects of data science problems in Biomedicine: 1) String algorithms for the efficient representation, search, comparison, composition and compression of large sets of strings, mostly originating from DNA or RNA Sequencing. This includes genome assembly, efficient index data structures for strings and graphs, alignment techniques as well as quantitative approaches. 2) Statistical models and algorithms for the assessment and functional analysis of individual genomic variations. this includes the identification of variants, prediction of functional effects, imputation and integration problems as well as the association with clinical phenotypes. 3) Models for organization and representation of large scale biomedical data. This includes ontolgy concepts, biomedical databases, sequence annotation and data compression.

Resources

Learning Materials (Links)

General Information

Language
English
Levels
MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
1 page (single side) of A4 paper is allowed for notes in the exam. The notes may be typed (font restriction: minimal font 10pt) or handwritten.
70% session examination, 30% project work; the final grade will be calculated as weighted average of both of these elements. As a compulsory continuous performance assessment task, the project work must be passed on its own and has a bonus/penalty function. It consists of two course projects that can be done in groups. Each group member will have to give a short presentation of their individual contributions.The projects/presentations are an integral part (30 hours of work, 1 credits) of the course and consists of a practical part. Participation is mandatory. Failing the project results in a failing grade for the overall examination of Computational Biomedicine (261-5100-00L).Students who fail to fulfil the project requirement have to de-register from the exam. Otherwise, they are not admitted to the exam and they will be treated as a no show.

Registration & Places

Max Places
120

Course Components

Type Title Time & Place Hours
lecture Computational Biomedicine
Hybrid lecture: This lecture will take place in person and online. For online lectures, reserved rooms will remain on campus for students to follow the course from there.
  • Tue 10:15-12:00 (ML F 39)
2 h weekly
exercise Computational Biomedicine
Hybrid lecture: This lecture will take place in person and online. For online lectures, reserved rooms will remain on campus for students to follow the course from there.
  • Tue 13:15-14:00 (ML F 39)
1 h weekly
independent project Computational Biomedicine No time listed 1 h weekly

Offered In