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Keynote

An integrative analysis of network motifs and gene expression data to discover TF-miRNA regulation

Cheng Li, Ph.D.

Associate Professor
Department of Biostatistics and Computational Biology
Dana-Farber Cancer Institute
Harvard School of Public Health

Abstract: Gene regulatory networks with regulatory circuits at different domains are the fundamental mechanism in phenotypic expression of the underlying genome. Regulation by transcription factors (TF) and microRNA (miRNA) are among the important domains. With the availability of genomic data in recent years, improved understanding of regulatory network motifs will be of vital value to find causal mechanisms of diseases such as cancer and neurodegeneration. One particular type of interest is the TF-miRNA-gene feed-forward loops (FFLs). We hypothesized that an integrative analysis of network motifs and gene expression data can discover novel TF-miRNA regulation relationships important in biological processes and disease states. We developed computational algorithms for identifying them by constructing candidate regulation networks and assessing their significance in the context of expression data. Our analysis successfully identified experimentally validated TF-miRNA-gene networks in published research related to Alzheimer's disease and cancer, as well as novel networks.

Bio: Dr. Cheng Li received his B.S. degree in computer science in 1995 at Beijing Normal University, and Ph.D. degree in statistics in 2001 at University of California at Los Angeles. He joined the Department of Biostatistics of Harvard School of Public Health and Dana-Farber Cancer Institute as an assistant professor in 2002 and associate professor in 2008. He has developed many novel gene expression and SNP microarray analysis and visualization methods, and implemented and maintained the widely used genomics analysis software dChip, which has been cited 1800 times. His current interests are how genomic changes in the cell promote the initiation and progression of cancer and neurological disorders, and classify the diseases for prognosis. See www.ChengLiLab.org for more information.

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