Covered Topics

In this seminar you will learn how biological networks can be inferred from data. We focus on the task of inferring so-called causal networks that provide the capabilities to check and deduce hypothesis about the behaviour of biological systems under outer influences. To this end, we discuss efficient models and robust inference algorithms.

As the discussed models are deeply rooted in probability theory and statistics a solid background in both fields is mandatory.

The seminar will be held as a block seminar the week before the lectures start.

Lecturer

Prof. Dr. Hans-Peter Lenhof

Teaching Assistants

Prerequisites Important

Conditions for Certificate

Registration

In order to participate in the seminar you are required to attend the first meeting.
There the topics are briefly discussed and distributed. No prior registration or reservation is possible.

Please inform yourself about the presented topics in order to ensure that you will get a topic that is to your liking and suits your abilities.

# Paper Autoren Redner Betreuer
1 Bayesian graphical models for genomewide association studies Verzilli et al.
2 Causal protein-signaling networks derived from multiparameter single-cell data Sachs et al. Sivarajan Karunanithi Lara Schneider
3 Causal reasoning on biological networks: interpreting transcriptional changes Chindelevitch et al.
4 Constrained hidden Markov models for population-based haplotyping Landwehr et al.
5 Inferring subnetworks from perturbed expression profiles Pe’er et al.
6 Sparse inverse covariance estimation with the graphical lasso Friedman et al. Koohyar Hadjisalimi Daniel Stöckel
First Meeting
Friday, 15.01.2016, 4:30 pm, Building E2.1, Room 406
Essay Deadline
Friday, 04.03.2016 11:59 pm
Slides Deadline
Friday, 01.04.2016, 11:59 pm
Talks
Thursday, 14.04.2016, 9:00 am - 12:00 pm, Building E2.1, Room 406

Checklist for slides

Checklist for report

Supplementary material

  1. How to give a scientific presentation (Susan McConnell) PDF PPT
  2. The Craft of Scientific Presentations (Michael Alley)