Monday, September 15, 2014
Computer Studies Bldg. Room 209
Georgia Institute of Technology
Parallel machine learning approaches for reverse engineering genome-scale networks
Reverse engineering whole-genome networks from large-scale gene expression measurements and analyzing them to extract biologically valid hypotheses are important challenges in systems biology. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. In this talk, I will present our research on the development of parallel mutual information and Bayesian network based structure learning methods to eliminate such bottlenecks and facilitate genome-scale network inference. As a demonstration, we reconstructed genome-scale networks of the model plant Arabidopsis thaliana from 11,700 microarray experiments using 1.57 million cores of the Tianhe-2 Supercomputer.
Bio: Srinivas Aluru is a professor in the School of Computational Science and Engineering at Georgia Institute of Technology. Earlier, he held faculty positions at Iowa State University, Indian Institute of Technology, New Mexico State University, and Syracuse University. He conducts research in high performance computing, bioinformatics and systems biology, combinatorial scientific computing, and applied algorithms. He pioneered the development of parallel methods in computational biology, and contributed to the assembly and analysis of complex plant genomes. Aluru is a recipient of the NSF career award, IBM faculty award, Swarnajayanti Fellowship from the Government of India, and the mid-career and outstanding research achievement awards from Iowa State University. He is a Fellow of the American Association for the Advancement of Science (AAAS) and the Institute for Electrical and Electronic Engineers (IEEE).