Jake Y. Chen, Ph.D.
School of Informatics, Indiana University
Department of Computer & Information Science, Purdue University
Time: 9:00 – 10:00AM, June 12, 2013
Place: FIT 1-312
ABSTRACT: Emerging properties of biomolecular interaction networks, such as network node centrality and clustering coeffient, have been well characterized in network biology studies in the past decade. However, how to take advantage of such characterization to help gain understanding of complex diseases, which often can be attributed to complex interaction of a variety of genetic and environmental risk factors, is not yet clear. We developed a series of network-based data analytic techniques to help analyze noisy and complex patterns from Omics measurement of disease samples. These techniques can improve robustness in detecting true signals, and therefore having a variety of biomedical applications in areas including: clinical biomarker discovery, drug repositioning, and side effect predictions.