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Statistical Models in Computational Biology
Last Updated: 2026-02-05 16:38:51
Abstract
The 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.
Objective
The 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.
Content
Graphical 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.
Resources
Lecture Notes
no
Literature
- 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
General Information
- Language
- English
- Levels
- BSC , DR , MSC , NDS
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 90 minutes
- Aids
- None
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture |
Statistical Models in Computational Biology
This lecture will take place online only (via Zoom). Link will be send to registered students in due time.
Online lecture: This lecture will primarily take place online. Reserved rooms will remain blocked on campus for students to follow the course from there.
|
|
2 h weekly |
| exercise |
Statistical Models in Computational Biology
This lecture will take place online only (via Zoom). Link will be send to registered students in due time.
|
|
1 h weekly |
| independent project |
Statistical Models in Computational Biology
Project work, no fixed presence required.
|
No time listed | 2 h weekly |
Offered In
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Computational Biology and Bioinformatics Master (More informations at: )
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Core Courses (Please note that the list of core courses is a closed list. Other courses cannot be added to the core course category in the study plan. Also the assignments of courses to core subcategories cannot be changed. Students need to pass at least one course in each core subcategory. A total of 40 ECTS needs to be acquired in the core course category.)
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Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)
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Doctorate Biosystems Science and Engineering (More Information at: )
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