636-0702-00L  Statistical Models in Computational Biology

SemesterFrühjahrssemester 2021
DozierendeN. Beerenwinkel
Periodizitätjährlich wiederkehrende Veranstaltung
LehrspracheEnglisch



Lehrveranstaltungen

NummerTitelUmfangDozierende
636-0702-00 VStatistical Models in Computational Biology
Starts at 12:15.

This course will be held online only via Zoom throughout the complete semester.

The lecturers will communicate the exact lesson times of ONLINE courses.
2 Std.
Do12:00-14:00ON LI NE »
N. Beerenwinkel
636-0702-00 UStatistical Models in Computational Biology
Starts at 14:15.

The tutorial will be held online only via Zoom throughout the complete semester.

The lecturers will communicate the exact lesson times of ONLINE courses.
1 Std.
Do14:00-15:00ON LI NE »
N. Beerenwinkel
636-0702-00 AStatistical Models in Computational Biology
Project work, no fixed presence required.
2 Std.N. Beerenwinkel

Katalogdaten

KurzbeschreibungThe course offers an introduction to graphical models and their application to complex biological systems. Graphical models combine a statistical methodology with efficient algorithms for inference in settings of high dimension and uncertainty. The unifying graphical model framework is developed and used to examine several classical and topical computational biology methods.
LernzielThe goal of this course is to establish the common language of graphical models for applications in computational biology and to see this methodology at work for several real-world data sets.
InhaltGraphical models are a marriage between probability theory and graph theory. They combine the notion of probabilities with efficient algorithms for inference among many random variables. Graphical models play an important role in computational biology, because they explicitly address two features that are inherent to biological systems: complexity and uncertainty. We will develop the basic theory and the common underlying formalism of graphical models and discuss several computational biology applications. Topics covered include conditional independence, Bayesian networks, Markov random fields, Gaussian graphical models, EM algorithm, junction tree algorithm, model selection, Dirichlet process mixture, causality, the pair hidden Markov model for sequence alignment, probabilistic phylogenetic models, phylo-HMMs, microarray experiments and gene regulatory networks, protein interaction networks, learning from perturbation experiments, time series data and dynamic Bayesian networks. Some of the biological applications will be explored in small data analysis problems as part of the exercises.
Skriptno
Literatur- Airoldi EM (2007) Getting started in probabilistic graphical models. PLoS Comput Biol 3(12): e252. doi:10.1371/journal.pcbi.0030252
- Bishop CM. Pattern Recognition and Machine Learning. Springer, 2007.
- Durbin R, Eddy S, Krogh A, Mitchinson G. Biological Sequence Analysis. Cambridge university Press, 2004

Leistungskontrolle

Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte6 KP
PrüfendeN. Beerenwinkel
FormSessionsprüfung
PrüfungsspracheEnglisch
RepetitionDie Leistungskontrolle wird in jeder Session angeboten. Die Repetition ist ohne erneute Belegung der Lerneinheit möglich.
Prüfungsmodusmündlich 20 Minuten
Zusatzinformation zum PrüfungsmodusRepetition possible only with re-enrollment, including projects.
The final grade is 70% oral session examination and 30% project. The practical projects are an integral part (60 hours of work, 2 credits) of the course. The project has to be re-run in case of a repetition.
Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan.

Lernmaterialien

Keine öffentlichen Lernmaterialien verfügbar.
Es werden nur die öffentlichen Lernmaterialien aufgeführt.

Gruppen

Keine Informationen zu Gruppen vorhanden.

Einschränkungen

Keine zusätzlichen Belegungseinschränkungen vorhanden.

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