Vanathi Gopalakrishnan, PhD

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. J Pathol Inform. 2014 Mar 28;5(1):12. doi: 10.4103/2153-3539.129448. eCollection 2014. PMID: 24860688. PMCID: PMC4030307.

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.

Jordan, R, Visweswaran, S, Gopalakrishnan, V. Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids. J Clin Bioinformatics. 2014, 4:13. PMID: 25379168 PMCID: PMC4215335.

Avali VR, Cooper GF, Gopalakrishnan V. Application of Bayesian logistic regression to mining biomedical data. In: Proceedings of the Fall Symposium of the American Medical Informatics Association (2014) Nov 14;2014:266-73. PMID: 25954328 PMC4419893

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.

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.

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]

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