Genomic and Proteomic Data: Analysis and Data Mining

Vanathi Gopalakrishnan, PhD is exploring the application of technology to the analysis of datasets from biological studies. She is fundamentally interested in technologies for data mining and discovery that allow incorporation of prior knowledge. Her research interests for the past decade have focused on the development, application, and evaluation of symbolic, probabilistic and hybrid machine learning methods to the modeling and analysis of high-dimensional, sparsely-populated biomedical datasets, particularly from proteomic profiling studies for early detection of disease. Her current research projects involve the study of novel variants of rule learning techniques for biomarker discovery, prediction and monitoring of neurodegenerative diseases, lung and breast cancers from molecular profiling studies. Methods for incorporating prior knowledge that are being researched in her laboratory include text mining and ontology construction. Gopalakrishnan is the recipient of a K25 award from the National Institute of General Medical Sciences.

Sample of Related Publications

Ryberg, H., An, J., Darko, S, Lustgarten, J.L., Jaffa, M.,  Gopalakrishnan, V.,  Lacomis, D, Cudkowicz, M, E., Bowser, R. Discovery and Verification of Amyotrophic Lateral Sclerosis Biomarkers by Proteomics. Muscle & nerve 2010;42(1):104-11. PMID: 20583124

Gopalakrishnan, V., Lustgarten, J. L., Visweswaran, S., Cooper, G.F. Bayesian Rule Learning for Biomedical Data Mining. Bioinformatics.  26(5) (2010) 668-675. PMID: 20080512; PMCID: PMC2852212

^