Niko Beerenwinkel: Catalogue data in Spring Semester 2018

Award: The Golden Owl
Name Prof. Dr. Niko Beerenwinkel
FieldComputational Biology
Address
Professur f. Computational Biology
ETH Zürich, BSS G 57.2
Klingelbergstrasse 48
4056 Basel
SWITZERLAND
Telephone+41 61 387 31 69
E-mailniko.beerenwinkel@bsse.ethz.ch
URLhttp://www.bsse.ethz.ch/cbg/people/nikob
DepartmentBiosystems Science and Engineering
RelationshipFull Professor

NumberTitleECTSHoursLecturers
636-0301-00LCurrent Topics in Biosystems Science and Engineering2 credits1ST. Stadler, N. Beerenwinkel, Y. Benenson, K. M. Borgwardt, P. S. Dittrich, M. Fussenegger, A. Hierlemann, D. Iber, M. H. Khammash, D. J. Müller, S. Panke, R. Paro, R. Platt, S. Reddy, T. Schroeder, J. Stelling
AbstractThis seminar will feature invited lectures about recent advances and developments in systems biology, including topics from biology, bioengineering, and computational biology.
ObjectiveTo provide an overview of current systems biology research.
ContentThe final list of topics will be available at http://www.bsse.ethz.ch/education/.
636-0702-00LStatistical Models in Computational Biology6 credits2V + 1U + 2AN. Beerenwinkel
AbstractThe 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.
ObjectiveThe 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.
ContentGraphical 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.
Lecture notesno
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