Faculty
Vanathi Gopalakrishnan, PhD
Research Interests
Dr. Gopalakrishnan is interested in the design and development of computational methods for solving clinically relevant biological problems. She is fundamentally interested in technologies for data mining and discovery that allow incorporation of prior knowledge. For the last decade she has developed and applied novel rule learning methods to biomarker discovery and verification from proteomic profiling studies. Her current research projects involve the development and application of novel variants of rule learning techniques to biomarker discovery and disease prediction for early detection and better understanding of mechanisms that cause neurodegenerative diseases, lung and breast cancers. Methods for incorporating prior knowledge that are being researched in her laboratory include text mining and ontology construction.
Appointments and Positions
Associate Professor of Biomedical Informatics
Associate Professor of Intelligent Systems
Associate Professor of Computational Biology
Biomedical Informatics Training Program Core Faculty
Current Research Projects and Collaborations
SPORE in Lung Cancer (Co-Director of Bioinformatics and Biostatistics CORE):The objectives of the UPCI Lung Cancer SPORE are to improve detection and treatment of lung cancer and to understand the mechanisms of increased susceptibility of women to lung cancer in collaboration with Dr. Jill Siegfried and Dr. Bill Bigbee.
The Center for ALS Research: In collaboration with Dr. Bowser who is involved in understanding molecular mechanisms that underlie Amyotrophic Lateral Sclerosis, the project includes learning of useful models for predicting disease onset and progression from integration of clinical information with genomic and proteomic markers.
Slaptamer-Based Array Technology for Discovery of Lung Cancer Serum Biomarkers: In collaboration with Dr. Jill Siegfried, Dr. Bill Bigbee and SomaLogic, Inc., involving the identification and annotation of serum samples from lung cancer patients to include both primary lung adenocarcinoma and squamous cell carcinoma of both early (stage I/II) and late (stage III/IV) cases in approximately equal numbers, patients with a spectrum of benign lung disease, patients with CT-detected nodules and healthy subjects matched to patients on the basis of age, gender, and smoking history. The provided samples will be evaluated on SomaLogic arrays utilizing reagents and protocols developed and standardized by SomaLogic. Exploratory data analysis will be conducted on the shared primary data set by both parties utilizing in house expertise and members of the Bioinformatics core.
Recent 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
Lustgarten, J.L., Visweswaran, S., Hogan, W.R., Gopalakrishnan, V. Knowledge Based Variable Selection for Learning Rules from Proteomic Data. BMC Bioinformatics. 2009 Sep 17;10 Suppl 9:S16. PMID: 19761570 PMCID: PMC2745687.
Lustgarten, J.L., Gopalakrishnan, V., Grover, H., Visweswaran, S. Measuring Stability of Feature Selection in Biomedical Datasets. In: Proceedings of the Fall Symposium of the American Medical Informatics Association (Nov 2009). 406-10. PMCID: PMC2815476