Vanathi Gopalakrishnan
Affiliations: | Biomedical Informatics | University of Pittsburgh, Pittsburgh, PA, United States |
Website:
https://www.dbmi.pitt.edu/person/vanathi-gopalakrishnan-phdGoogle:
"Vanathi Gopalakrishnan"Bio:
Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave, Parkvale M-183, Pittsburgh, PA, USA
Parents
Sign in to add mentor| Bruce Gardner Buchanan | grad student | 1999 | University of Pittsburgh (Computer Science Tree) | |
| (Parallel Experiment Planning: Macromolecular Crystallization Case Study) | ||||
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Publications
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| Balasubramanian JB, Boes RD, Gopalakrishnan V. (2020) A novel approach to modeling multifactorial diseases using Ensemble Bayesian Rule Classifiers. Journal of Biomedical Informatics. 103455 |
| Balasubramanian JB, Gopalakrishnan V. (2018) Tunable structure priors for Bayesian rule learning for knowledge integrated biomarker discovery. World Journal of Clinical Oncology. 9: 98-109 |
| Ogoe HA, Visweswaran S, Lu X, et al. (2015) Knowledge transfer via classification rules using functional mapping for integrative modeling of gene expression data. Bmc Bioinformatics. 16: 226 |
| Li X, LeBlanc J, Truong A, et al. (2011) A metaproteomic approach to study human-microbial ecosystems at the mucosal luminal interface. Plos One. 6: e26542 |
| Ganchev P, Malehorn D, Bigbee WL, et al. (2011) Transfer learning of classification rules for biomarker discovery and verification from molecular profiling studies. Journal of Biomedical Informatics. 44: S17-23 |
| Gopalakrishnan V, Lustgarten JL, Visweswaran S, et al. (2010) Bayesian rule learning for biomedical data mining. Bioinformatics (Oxford, England). 26: 668-75 |
| Zeng X, Hood BL, Zhao T, et al. (2010) Abstract 4564: Lung cancer serum biomarker discovery using label free LC-MS/MS Cancer Research. 70: 4564-4564 |
| Lustgarten JL, Visweswaran S, Bowser RP, et al. (2009) Knowledge-based variable selection for learning rules from proteomic data. Bmc Bioinformatics. 10: S16 |
| Liu Y, Carbonell J, Gopalakrishnan V, et al. (2009) Conditional graphical models for protein structural motif recognition. Journal of Computational Biology : a Journal of Computational Molecular Cell Biology. 16: 639-57 |
| Liu Y, Carbonell J, Weigele P, et al. (2006) Protein fold recognition using segmentation conditional random fields (SCRFs). Journal of Computational Biology : a Journal of Computational Molecular Cell Biology. 13: 394-406 |