261-5100-00L  Computational Biomedicine

SemesterHerbstsemester 2020
DozierendeG. Rätsch, V. Boeva, N. Davidson
Periodizitätjährlich wiederkehrende Veranstaltung
LehrspracheEnglisch
KommentarNumber of participants limited to 60.



Lehrveranstaltungen

NummerTitelUmfangDozierende
261-5100-00 VComputational Biomedicine
The lecturers will communicate the exact lesson times of ONLINE courses.
2 Std.
Di10-12ON LI NE »
G. Rätsch, V. Boeva, N. Davidson
261-5100-00 UComputational Biomedicine
The lecturers will communicate the exact lesson times of ONLINE courses.
1 Std.
Di13-14ON LI NE »
G. Rätsch, V. Boeva, N. Davidson
261-5100-00 AComputational Biomedicine1 Std.G. Rätsch, V. Boeva, N. Davidson

Katalogdaten

KurzbeschreibungThe course critically reviews central problems in Biomedicine and discusses the technical foundations and solutions for these problems.
LernzielOver 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.
InhaltThe 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.
Voraussetzungen / BesonderesData Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line

Leistungskontrolle

Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte5 KP
PrüfendeG. Rätsch, V. Boeva
FormSessionsprüfung
PrüfungsspracheEnglisch
RepetitionDie Leistungskontrolle wird in jeder Session angeboten. Die Repetition ist ohne erneute Belegung der Lerneinheit möglich.
Prüfungsmodusschriftlich 180 Minuten
Zusatzinformation zum Prüfungsmodus70% 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.
Hilfsmittel schriftlich1 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.
Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan.

Lernmaterialien

 
HauptlinkCourse webpage
Es werden nur die öffentlichen Lernmaterialien aufgeführt.

Gruppen

Keine Informationen zu Gruppen vorhanden.

Einschränkungen

PlätzeMaximal 75
WartelisteBis 28.09.2020

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