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Scientific breakthroughs from genome-sequencing projects brought the realization that reliable interpretation of the resulting information makes unprecedented demands for contemporaneous advances in computation and mathematical modeling. As the complexity of genomic data sets drives innovative statistics research, the new Statistical Machine Learning in Functional Genomics (Statomics) Lab of the Ottawa Institute of Systems Biology aims to develop and apply novel methodology and algorithms to solve current problems in analyzing gene-expression, proteomics, metabolomics, SNP, ChIP-chip, and/or phenotypic data. The lab is presently targeting the inference of regulatory networks from multiple sources of information and improvements in the repeatability of microarray results and will attack similar statistics and machine learning challenges of importance to functional genomics.

The word statomics abbreviates *stat*istical machine learning in functional gen*omics*.

David Bickel launched the Statomics Lab in June of 2007 at the Ottawa Institute of Systems Biology.

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David Bickel

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bioinformatics systems biology statistical genomics machine learning bayesian inference microarray data analysis snps life-science technology health-medicine interdisciplinary-research engineering-computer-science general-science-other