261-5100-00L  Computational Biomedicine

SemesterAutumn Semester 2021
LecturersV. Boeva, G. Rätsch
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber of participants limited to 120.


261-5100-00 VComputational 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.
2 hrs
Tue10:15-12:00ML F 39 »
V. Boeva, G. Rätsch
261-5100-00 UComputational 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.
1 hrs
Tue13:15-14:00ML F 39 »
V. Boeva, G. Rätsch
261-5100-00 AComputational Biomedicine1 hrsV. Boeva, G. Rätsch

Catalogue data

AbstractThe course critically reviews central problems in Biomedicine and discusses the technical foundations and solutions for these problems.
ObjectiveOver 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.
ContentThe 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.
Prerequisites / NoticeData Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits5 credits
ExaminersG. Rätsch, V. Boeva
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 120 minutes
Additional information on mode of examination70% 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.
Written aids1 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.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

Main linkCourse webpage
Only public learning materials are listed.


No information on groups available.


Places120 at the most
Waiting listuntil 04.10.2021

Offered in

Cyber Security MasterElective CoursesWInformation
Data Science MasterInterdisciplinary ElectivesWInformation
Computer Science MasterElective CoursesWInformation
Computer Science MasterFocus Elective Courses General StudiesWInformation
Computational Science and Engineering MasterElectivesWInformation