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.

Associate Professor of Biomedical Informatics
Associate Professor of Intelligent Systems
Associate Professor of Computational and Systems Biology
Director, PRoBE Laboratory for Pattern Recognition from Biomedical Evidence
Core Faculty Member, Biomedical Informatics Training Program
Faculty Member, Intelligent Systems Program
Faculty Member, Joint CMU-Pitt Program in Computational Biology
Faculty Member, Medical Scientist Training Program
Faculty Member, Cardiovascular Bioengineering Training Program
Co-Director of Bioengineering, Biotechnology and Innovation (BBI) Area of Concentration, School of Medicine (About)
 
Selected 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.

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.

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.

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]

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

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

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.

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

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

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

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

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