Dana Pe’er, Columbia University In the City of New York, USA

Computational Learning Methods and Biological Networks

Those lectures will concentrate on the main topics:

  1. Bayesian Networks for molecular networks: Background and rationale.

  2. Module Networks, modularity in biology and the statistical power gaining. Interpretation of modules. Intro to incorporating sequence and genetic variation into module networks.

  3. Machine learning applications to genetic genomics.

 

Bibliography:

Pe'er D.Bayesian network analysis of signaling networks: a primer, Science STKE, 281:pl4, April 2005

Segal, E*., Shapira, M., Regev, A*., Pe'er, D., Botstein, D., Koller, D. and Friedman, N., (*equal contribution), Module networks: identifying regulatory modules and their condition specific regulators from gene expression data, Nature Genetics 34:166-176, June 2003.

Eran Segal, Haidong Wang, and Daphe Koller, Discovering Molecular Pathways from Protein Interaction and Gene Expression Data.  ISMB03

Lee S.*, Pe'er D., Dudley A., Church G., and Koller, D. (*equal contribution), Identifying Regulatory Mechanisms and their Individual Variation Reveals Key Role of Chromatin Modification, Proc Natl Acad Sci.2006 Sep 19;103(38):14062-7.

Litvin O., Causton H., Chen BJ., Pe'er D., Modularity and interactions in the genetics of gene expression Proc Natl Acad Sci. 2009 Feb 17

Lee S., Dudley A., Drubin D., Silver P., Krogan N., Pe'er D. and Koller D., Learning a Prior on Regulatory Potential from eQTL Data, PLoS Genet 5 (1), e1000358, 2009.

Sachs K., Itani S., Carlisle J., Nolan G., Pe'er D. and Lauffenburger D., Learning Signaling Network Structures with Sparsely Distributed Data, Journal of Computational Biology, 2009.