University of Pittsburgh logo
  1. People

Vanathi Gopalakrishnan

  • Associate Professor, Department of Biomedical Informatics

Dr. Gopalakrishnan is a biomedical data scientist who is passionate about developing intelligent systems to reduce the burden of disease. Her primary research focus has been on the development of novel algorithms involving rule learning for the predictive and integrative modeling of biomedical data obtained from molecular profiling studies, radiologic imaging and clinical textual reports. She is fundamentally interested in technologies for data mining and discovery that allow incorporation of prior knowledge. Fundamental research areas of interest involve extensions to rule learning via the incorporation of (1) Bayesian Statistics, (2) prior rule models, and (3) knowledge obtained through mining of ontologies or the literature. Dr. Gopalakrishnan is generally interested in the design and development of computational methods for solving clinically relevant biological problems, such as the discovery and verification of biomarkers for disease state prediction. Her research over the past decade has 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 diverse diseases including neurodegenerative and cardiovascular diseases, lung, breast and esophageal cancers, and parasitic infectious disease, with a focus on the analyses of data obtained from metabolomics and microbiome profiling.

Institution of Highest Degree

  • University of Pittsburgh

Representative 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.

Research Interests

Rule Learning Hybrid Algorithms - Design and Development
Multi-modal Biomedical Data Science - Modeling and Analysis
Biomarker Discovery
Predictive Modeling for Precision Medicine and Health Care