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Computational Laboratory of Integrative Genomics
Location :University of Southern California, Los Angeles, California, USA.
(1) Method development
We develop computational and statistical methods for the integrative analysis of diverse genomic sources, including microarray data, proteomics data, sequence data, and the text data. In particular:
Develop algorithms for network-based data mining: Although many methods are available for the analysis of a single network, few algorithms exist for mining patterns across many massive networks. We have been developing a series of algorithms to mine frequent patterns across many biological networks. Utilizing these algorithms, we perform large-scale functional annotation and regulatory network reconstruction for yeast, mouse, and human.
Develop methods for the integrative analysis of cross-platform microarray data: The rapid accumulation of microarray gene expression data translates into an urgent need for methods that can effectively integrate data generated by different platforms. Continuing our previous effort, we have been developing novel methods to integrate and analyze cross-platform microarray data.
(2) Biological discovery
We are applying the above developed methods to study particular biological systems, with models including aging and cancer.
Cancer: Many studies have identified genes differentially expressed between cancer and normal tissues. However, phenotypes such as cancer are determined not only by individual genes, but also by the underlying structure of genetic networks. Often, it is the interaction of genes that causes phenotypic differences. We develop methods to determine the genetic network signature of cancer by integrating multiple microarray data sets.
Aging: By integrating public microarray data sets related to aging, we try to identify novel genes and regulatory pathways involved in the aging process.
(3) Software development
We develop software for the integrative analysis of diverse data sets/types:
Integrative Array Analyzer: This is the first software package to perform integrative analysis of cross-platform and cross-species microarray datasets.
Gene Aging Nexus: We are developing a web-based data mining platform Gene Aging Nexus freely accessible to the biogerontological-geriatric research community to query/analyze/visualize various aging-related genomic data sources, in particular, microarray data.