Bioinformatics 2

Course Description

This course introduce students with: (i) basic probabilistic graphical models such as markov random fields and Bayesian networks (ii) hidden Markov models (iii) Bayes and causal inference (iv) application of the knowledge gained on problems in biology.

Learning Outcomes

  1. Describe and apply basic probabilistic graphical models such as markov random fields and Bayesian networks
  2. Explain hidden markov models
  3. Apply Bayes and causal inference to solving tasks
  4. Apply basic probabilistic graphical models to solving common biological problems

Forms of Teaching

Lectures

Lectures in the classroom

Independent assignments

Project team work.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Seminar/Project 0 % 40 % 0 % 40 %
Mid Term Exam: Written 0 % 25 % 0 %
Final Exam: Written 40 % 35 %
Exam: Written 40 % 60 %

Week by Week Schedule

  1. Graphical models for computational biology
  2. Graphical models for computational biology
  3. Graphical models for computational biology
  4. Graphical models for computational biology
  5. Graphical models for computational biology
  6. Hidden Markov models for computational biology
  7. Hidden Markov models for computational biology
  8. Midterm exam
  9. Hidden Markov models for computational biology
  10. Bayesian inference for computational biology
  11. Bayesian inference for computational biology
  12. Bayesian inference for computational biology
  13. Application to complex biological systems
  14. Application to complex biological systems
  15. Final exam

Study Programmes

University graduate
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Literature

(.), Probabilistic Graphical Models : Principles and Techniques, Daphne Koller and Nir Friedman,
Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison (.), Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Cambridge University Press

For students

General

ID 222485
  Winter semester
5 ECTS
L3 English Level
L1 e-Learning
30 Lectures
5 Laboratory exercises

Grading System

90 Excellent
75 Very Good
60 Good
50 Acceptable