ISCB-Asia/SCCG 2012, session on cancer genome informaticsShihua Zhang
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We adopt a joint matrix factorization technique to address this challenge. This method projects multiple types of genomic data onto a common coordinate system, in which heterogeneous variables weighted highly in the same projected direction form a multi-dimensional module. Genomic variables in such modules are characterized by significant correlations and likely functional associations. We applied this method to the DNA methylation, gene expression, and microRNA expression data of 385 ovarian cancer samples from the TCGA project. These multi-dimensional modules revealed perturbed pathways that would have been overlooked with only a single type of data, uncovered associations between different layers of cellular activities, and allowed the identification of clinically distinct patient subgroups. Our study provides an useful protocol for uncovering hidden patterns and their biological implications in multi-dimensional ``omic" data.
His interests are within Bioinformatics, Computational Biology and Network Science, particularly in Cancer Genomics and Network Biology. He has published over 30 technical papers in the refereed journals and conference proceedings such as in Bioinformatics (including two ISMB papers), PLoS Computational Biology, Nucleic Acids Research, Proteomics, Plos ONE, BMC Systems Biology, BMC Bioinformatics, Physical Review E.