Mining Genomic Data for Cancer Biomarker Prediction
Dr. Yang Xiang, Ph.D.
Research Assistant Professor
Department of Biomedical Informatics
The Ohio State University
Columbus, Ohio
March 22nd @ 3:45 p.m. 228 MSB
Abstract:
High throughput genomic data such as gene expression, microRNA, and DNA methylation can be used to build gene networks in the form of undirected graphs or bipartite graphs. We have built correlation networks on data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and used graph mining algorithms to search for network patterns that correspond to potential biomarkers for diseases such as breast cancer, glioblastoma, and colon cancer. Accordingly, we have identified gene networks that correspond to specific molecular functions or biological processes. The survival tests on clinical data show that many of them have good prognosis powers for cancers. Our results suggest that network mining and analysis on molecular data is promising for understanding cancer physiology, predicting new gene functions, and providing potential biomarkers for cancer therapeutics.
Bio:
Yang Xiang is a Research Assistant Professor in the Department of Biomedical Informatics, The Ohio State University. He received his PhD degree in Computer Science from Kent State University in December 2009, and joined The Ohio State University Comprehensive Cancer Center in January 2010 as a Postdoctoral Researcher. In 2010, he received the NSF/CRA/CCC Computing Innovation Fellow award and had been supported by the NSF for the CIFellows project for 2 years. His research interests include translational bioinformatics, clinical informatics, computational biology, graph databases, data/graph mining, algorithmic graph theory, and visualization.