636-0702-00L  Statistical Models in Computational Biology

SemesterFrühjahrssemester 2022
DozierendeN. Beerenwinkel
Periodizitätjährlich wiederkehrende Veranstaltung


636-0702-00 VStatistical Models in Computational Biology
This lecture will take place online only (via Zoom). Link will be send to registered students in due time.
Room reserved in Basel: Oppenheim (BSD G205)
Rom reserved in Zürich: HG D 16.2
2 Std.
Do12:15-14:00BSD G 205 »
12:15-14:00HG D 16.2 »
N. Beerenwinkel
636-0702-00 UStatistical Models in Computational Biology
This lecture will take place online only (via Zoom). Link will be send to registered students in due time.
Room reserved in Basel: Oppenheim (BSD G205)
Rom reserved in Zürich: HG D 16.2
1 Std.
Do14:15-15:00BSD G 205 »
14:15-15:00HG D 16.2 »
N. Beerenwinkel
636-0702-00 AStatistical Models in Computational Biology
Project work, no fixed presence required.
2 Std.N. Beerenwinkel


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.
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


Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte6 KP
PrüfendeN. Beerenwinkel
RepetitionDie Leistungskontrolle wird nur in der Session nach der Lerneinheit angeboten. Die Repetition ist nur nach erneuter Belegung 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.


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


Keine Informationen zu Gruppen vorhanden.


Keine zusätzlichen Belegungseinschränkungen vorhanden.

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