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Statistical network analysis in computational genomics

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I will give a general introduction to two prominent fields of functional genomics and illustrate them by examples from my own research.

Part 1: gene perturbation screens: Functional genomics has demonstrated considerable success in inferring the inner working of a cell through analysis of its response to various perturbations, like drug treatment, DNA mutations or RNA interference. In recent years several technological advances have pushed gene perturbation screens to the forefront of functional genomics. I will give a short overview of computational and statistical challenges arising from gene perturbation screens, focussing on the functional annotation of observed phenotypes in the context of cellular pathways and networks. This part is based on my ISMB 2009 tutorial (see

Part 2: cancer genomics. With collaboration partners at the CRI we are working on characterizing the genomic landscape of breast cancer. For 1000 tumours (and in the future 2000) we integrate complementary genomic data (ranging from DNA mutations and chromosomal copy number variation to gene and microRNA expression) into a global picture of the heterogeneity of the disease. The goal is to identify the molecular pathways and networks driving cancer and to stratify the patient population into homogeneous sub-groups.

This talk is part of the Microsoft Research Cambridge, general interest public talks series.

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