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Trumbull News Detail

CS Colloquium 3/22/2013 "Mining Genomic Data for Cancer Biomarker Prediction"

Posted Mar. 13, 2013

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.