Vanathi Gopalakrishnan, PhD

Room 530
5607 Baum Boulevard
Pittsburgh, PA 15206
Phone Number: 
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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

Transfer Rule Learning for Knowledge Based Biomarker Discovery and Predictive Biomedicine: This project will develop highly-needed computational methods for integrative biomarker discovery from related but separate data sets produced by predictive molecular profiling studies of disease. It will generate new experimental data for early detection of lung cancer, and has the potential to help create new diagnostic screening tools for lung cancer, a leading cause of death from cancer in the United States.

Bayesian Rule Learning Methods for Disease Prediction and Biomarker Discovery: This project will develop highly-needed data mining methods for analyzing the spate of datasets arising from high-throughput technologies for molecular biomarker profiling. It will generate new experimental data for early detection of breast cancer, and has the potential to help create new diagnostic screening tools for three diverse diseases: two of the most common cancers in the world - lung and breast cancers, and rare, neurodegenerative Amyotrophic Lateral Sclerosis.

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.


Recent Publications

Menon PG, Morris L, Staines M, Lima J, Lee DC, Gopalakrishnan V. Novel MRI-derived quantitative biomarker for cardiac function applied to classifying ischemic cardiomyopathy within a Bayesian rule learning framework. Proceedings of the SPIE Medical Imaging 2014; February 15-20, 2014; San Diego, CA, USA. 2014.

Gopalakrishnan V, Menon PG, Madan S. A novel framework to enhance scientific knowledge of cardiovascular MRI biomarkers and their application to pediatric cardiomyopathy classification. Proceedings of the Second International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2014); Granada, Spain. 2014. p. (8 pages). In Press.

Balasubramanian JB, Cooper GF, Visweswaran S, Gopalakrishnan V. Selective Model Averaging with Bayesian Rule Learning for Predictive Biomedicine. Proceedings of the AMIA 2014 Joint Summits in Translational Science (In Press); April 2014; San Francisco, CA, USA2014.

Dutta-Moscato J, Gopalakrishnan V, Lotze MT, Becich MJ. Creating a Pipeline of Talent for Informatics: STEM Initiative for High School Students in Computer Science, Biology and Biomedical Informatics (CoSBBI). Journal of Pathology Informatics. 2014; In Press. PMC In Process.

McMillan A, Visweswaran S, Gopalakrishnan V. Machine Learning for Biomarker-based Classification of Alzheimer's Disease Progression Journal of Pathology Informatics. 2014; In Press.

Staines M, Morris L, Menon PG, Lima J, Lee DC, Gopalakrishnan V. Discovering Biomarkers for Cardiovascular Disease Using Rule Learning. Journal of Pathology Informatics. 2014; In Press.

Floudas, C. S., Balasubramanian, J, Romkes, M., Gopalakrishnan, V. An empirical workflow for genome-wide single nucleotide polymorphism-based predictive modeling. In the Proceedings of the AMIA Translational Bioinformatics Summit 2013, March 18-20, San Francisco, CA.

Grover H, Wallstrom G, Wu CC, Gopalakrishnan V. Context-Sensitive Markov Models for Peptide Scoring and Identification from Tandem Mass Spectrometry. Omics : a journal of integrative biology. 2013 Feb;17(2):94-105. doi: 10.1089/omi.2012.0073. Epub 2013 Jan 5 PMID: 23289783 PMCID: PMC3567622 [Available on 2014/2/1]

Grover, H., Gopalakrishnan, V. Efficient Processing of Models for Large-scale Shotgun Proteomics Data. In Proceedings of the International Workshop on Collaborative Big Data (C-Big 2012), Pittsburgh, PA, October 14, 2012.

Liu, G., Kong, L., Gopalakrishnan, V. A Partitioning Based Adaptive Method for Robust Removal of Irrelevant Features from High-dimensional Biomedical Datasets. In Proceedings of the 2012 AMIA Summit on Translational Bioinformatics. San Francisco, March 19-23, 2012. Pages 52-61. PMCID: PMC3392052

Bigbee, W. L*., Gopalakrishnan, V*, Weissfeld J, L., Wilson, D. O., Dacic, S. Lokshin, A. E., Siegfried, J. M.  A Multiplexed Serum Biomarker Immunoassay Panel Discriminates Clinical Lung Cancer Patients from High-Risk Individuals Found to be Cancer-Free by CT Screening. J Thorac Oncol. 2012 Apr;7(4):698-708. (*These authors contributed equally to the study). PMID: 22425918 PMCID: PMC3308353

Li, X., LeBlanc, J., Truong, A., Vuthoori, R., Chen, S. S., Lustgarten, J. L., Roth, B., Allard, J., Andrew Ippoliti, A., Presley, L.L., Borneman, J., Bigbee, W.L., Gopalakrishnan, V., Graeber, T.G., Elashoff, D., Braun, J., Goodglick, L. A Metaproteomic Approach to Study Human-Microbial Ecosystems at the Mucosal Luminal Interface. 2011. PLoS ONE 6(11): e26542. PMCID:PMC3221670

Lustgarten, J. L., Visweswaran, S., Gopalakrishnan, V., Cooper, G. F. Application of an Efficient Bayesian Discretization Method to Biomedical Data. BMC Bioinformatics. 2011 Jul 28;12:309. PMCID: PMC3162539

Ganchev, P., Malehorn, D., Bigbee, W. L., Gopalakrishnan, V. Transfer Learning of Classification Rules for Biomarker Discovery and Verification from Molecular Profiling Studies. J Biomed Inform. 2011 Dec;44 Suppl 1:S17-23. Epub 2011 May 6. (Won a Distinguished Paper Award at AMIA 2011 - Translational Bioinformatics) PMID: 21571094

Zeng, X., Hood, B.L., Zhao, T., Conrads, T.P., Sun, M., Gopalakrishnan, V., Grover, H., Day, R.S., Weissfeld, J.L., Siegfried, J.M., Bigbee W.L. Lung Cancer Serum Biomarker Discovery Using Label Free Liquid Chromatography-Tandem Mass Spectrometry. J Thorac Oncol. 2011 Apr;6(4):725-34. PMCID:PMC3104087

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

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