261-5100-00L  Computational Biomedicine

SemesterAutumn Semester 2019
LecturersG. Rätsch, V. Boeva, N. Davidson
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber of participants limited to 60.


261-5100-00 VComputational Biomedicine2 hrs
Tue10:15-12:00LEE E 101 »
G. Rätsch, V. Boeva, N. Davidson
261-5100-00 UComputational Biomedicine1 hrs
Tue13:15-14:00CAB G 56 »
G. Rätsch, V. Boeva, N. Davidson
261-5100-00 AComputational Biomedicine1 hrsG. Rätsch, V. Boeva, N. Davidson

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, N. Davidson
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examinationwritten 120 minutes
Additional information on mode of examinationBeside the session exam, there will be two course projects that can be done in groups. Each group member will have to give a short presentation of their individual contributions. Based on the contribution and presentation, up to 0.25 grade points can be gained towards the overall score.
Written aids1 sheet A4
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.


Places60 at the most
Waiting listuntil 30.09.2019

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