The course critically reviews central problems in Biomedicine and discusses the technical foundations and solutions for these problems.
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.
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.
Prerequisites / Notice
Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line
Performance assessment information (valid until the course unit is held again)
The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examination
written 180 minutes
Additional information on mode of examination
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.
1 page (single side) of A4 paper is allowed for notes in the exam. There is no further restriction on the notes. They may be handwritten or typed and there is no font restriction.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.