Vanathi Gopalakrishnan, PhD
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.
Areas of Interest:
Rule Learning Hybrid Algorithms - Design and Development,
Multi-modal Biomedical Data Science - Modeling and Analysis,
Predictive Modeling for Precision Medicine and Health Care
Associate Professor of Biomedical Informatics
Associate Professor of Intelligent Systems
Associate Professor of Computational and Systems Biology
Associate Professor of Bioengineering
Associate Professor of Clinical and translational Science
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
Director of the Intelligent Systems Program, School of Computing and Information
Balasubramanian JB, Boes RD, Gopalakrishnan V. A novel approach to modeling multifactorial diseases using Ensemble Bayesian Rule classifiers. Journal of Biomedical Informatics 020 Jul;107:103455. doi: 10.1016/j.jbi.2020.103455. PMID: 32497685
Y Liu, J Manners, Y Bittar, SHY Chou, V Gopalakrishnan. Towards precision critical care management of blood pressure in hemorrhagic stroke patients using dynamic linear models. PLoS One 2019;14(8): e0220283.
Balasubramanian JB, Gopalakrishnan V. Tunable structure priors for Bayesian rule learning for knowledge integrated biomarker discovery.World J Clin Oncol. 2018 Sep 14;9(5): 89-109. doi: 10.5306/wjco.v9.i5.98. PMID: 30254965. PMC ID: PMC6153126.
Ceschin R, Zahner A, Reynolds W, Gaesser J, Zuccoli G, Lo CW, Gopalakrishnan V, Panigrahy A. A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks. Neuroimage. 2018 Sep;178:183-197. doi: 10.1016/j.neuroimage.2018.05.049. Epub 2018 May 21. PMID: 29793060.
Lustgarten JL, Balasubramanian JB, Visweswaran S, Gopalakrishnan V, Learning Parsimonious Classification Rules from Gene Expression Data Using Bayesian Networks with Local Structure. Data (Basel). 2017 Mar;2(1). pii: 5. 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.
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.
R. Ceschin, A. Panigrahy, and V. Gopalakrishnan, “Sfdm: Open-source software for temporal analysis and visualization of brain tumor diffusion mr using serial functional diffusion mapping,” Cancer Inform., vol. 14, pp. 1–9, 2015. PMID: 25673970 PMCID: PMC4315050 DOI: 10.4137/CIN.S17293
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 BL, Zhao T, Conrads TP, Sun M, Gopalakrishnan V, Grover H, Day RS, Weissfeld JL, Siegfried JM, Bigbee WL. 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