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Biography

Institution of Highest Degree:
University of Pittsburgh

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.

Research Interests

Bioinformatics - Gene Expression Analysis
Machine Learning - Symbolic Inductive Learning from Temporal Data, Data Mining
Scientific Experiment Design and Planning
Macromolecular Crystallization

Recent Publications

Lustgarten JL, Balasubramanian JB, Visweswaran S, Gopalakrishnan V, Learning Parsimonious Classification Rules from Gene Expression Data Using Bayesian Networks with Local Structure.  2017 Mar;2(1). pii: 5. doi: 10.3390/data2010005. Epub 2017 Jan 18.    PMID: 28331847  PMCID:  PMC5358670  DOI: 10.3390/data2010005

Liu Y, Gopalakrishnan V. An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data. Data 2017, 2(1), 8; doi: 10.3390/data2010008.

Pineda AL, Ogoe HA, Balasubramanian JB, Rangel Escareño C, Visweswaran S, Herman JG, Gopalakrishnan V. On Predicting lung cancer subtypes using 'omic' data from tumor and tumor-adjacent histologically-normal tissue.  BMC Cancer. 2016 Mar 4;16:184. doi: 10.1186/s12885-016-2223-3. PMID:  26944944  PMCID:  PMC4778315  DOI: 10.1186/s12885-016-2223-3

Torbati ME, Mitreva M, Gopalakrishnan V. Application of Taxonomic Modeling to Microbiota Data Mining for Detection of Helminth Infection in Global Populations.  2016 Dec;1(3). pii: 19. doi: 10.3390/data1030019. Epub 2016 Dec 13.    PMID: 28239609  PMCID: PMC5325162  DOI: 10.3390/data1030019

Gopalakrishnan V, Menon PG, Madan S., cMRI-BED: A novel informatics framework for cardiac MRI biomarker extraction and discovery applied to pediatric cardiomyopathy classification. Biomed Eng Online. 2015;14 Suppl 2:S7. doi: 10.1186/1475-925X-14-S2-S7. Epub 2015 Aug 13. PMID: 26329721 PMCID: PMC4547147 DOI: 10.1186/1475-925X-14-S2-S7

Ogoe, HA, Visweswaran, S, Lu, X, Gopalakrishnan, V.  (2015) Knowledge transfer via classification rules using functional mapping for integrative modeling of gene expression data.  BMC Bioinformatics 16:226 (designated as a Highly Accessed paper) PMID: 26202217 PMCID: PMC4512094

Pineda, AL, Gopalakrishnan, V. Novel Application of Junction Trees to the Interpretation of Epigenetic Differences among Lung Cancer Subtypes. Proceedings of the AMIA Translational Bioinformatics Summit. March 21-23, 2015. PMID: 26306226   Winner of the Marco Ramoni Distinguished Paper Award.

Last updated: August 4, 2017