Assistant Professor of Biomedical Informatics

Assistant Professor of Intelligent Systems

Assistant Professor of Computational Biology

Contact Info:

Research Interests

Gopalakrishnan is interested in the design and development of computational methods for solving clinically relevant biological problems. Her current research projects involve the application of machine learning and data mining techniques to the analysis of proteomic data, prediction of protein sequence-structure-function relationships, and the study of variables affecting protein crystallization.

Education

• MS, University of Pittsburgh - 1991
• PhD, University of Pittsburgh - 1999

Selected Publications

Gopalakrishnan, V. Computer Aided Knowledge Discovery in Biomedicine. Invited Book Chapter in Handbook of Research on Systems Biology Applications in Medicine. Daskalaki, A. (Ed.), (Information Science Reference, 2008).

Lustgarten, J. L., Grover, H., Visweswaran, S., Gopalakrishnan, V. An Evaluation of Discretization Methods for Learning Rules from Biomedical Datasets. In Proceedings of The 2008 International Conference on Bioinformatics And Compuational Biology (BIOCOMP’08). (2008). Editors Hamid R. Arabnia, Mary Qu Yang, Jack Y. Yang. pp. 527-532

Grover, H., Lustgarten, J., Visweswaran, S., Gopalakrishnan, V. Improving Peptide Identification via Validation with Intensity-based Modeling of Tandem Mass Spectra. In Proceedings of the International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics (BCBGC-08). (2008).pp. 56-63.

Liu, Y., Carbonell, J., Gopalakrishnan, V., Weigele, P. Discriminative Graphical Models for Protein Quaternary Structure Motif Detection. In Proceedings of ICML-07 Workshop on Constrained Optimization and Structured Output Space (2007).

Liu, Y., Carbonell, J., Gopalakrishnan, V., Weigele, P. Protein Quaternary Fold Recognition using Conditional Graphical Models. In Proceedings of the Twentieth Joint Conference on Artificial Intelligence (IJCAI-07) (2007) pp. 937-943

Mitra, P.S., Gopalakrishnan, V., McNamee, R.L. Segmentation of MRI Data by Maximization of Region Contrast. In Proceedings of Computer Vision and Pattern Recognition Workshop (CVPRW’06) (2006) 88. Gopalakrishnan V, Ganchev P, Ranganathan S, Bowser R. Rule Learning for Disease-Specific Biomarker Discovery from Clinical Proteomic Mass Spectra. BioDM. 2006: 93-105

Liu Y, Carbonell J, Weigele P, Gopalakrishnan V. Protein Fold Recognition using Segmentation Conditional Random Fields (SCRFs). J Comput Biol. 2006 Mar;13(2):394-406. PMID: 16597248

Ranganathan S, Williams E, Ganchev P, Gopalakrishnan V, Lacomis D, et al. Proteomic Profiling of Cerebrospinal Fluid Identifies Biomarkers for Amyotrophic Lateral Sclerosis. J. Neurochem. 2005 Dec;95(5):1461-71. PMID: 16313519

Liu Y, Carbonell J, Weigele P, Gopalakrishnan V. Segmentation Conditional Random Fields (SCRFs): A New Approach for Protein Fold Recognition. RECOMB 2005, 408-422.

Gopalakrishnan V, Livingston G, Hennessy D, Buchanan B, Rosenberg JM. Machine-learning techniques for macromolecular crystallization data. Acta Crystallogr D Biol Crystallogr. 2004 Oct;60(Pt 10):1705-16. Epub 2004 Sep 23. PMID: 15388916

Lu X, Zhai C, Gopalakrishnan V, Buchanan BG. Automatic annotation of protein motif function with Gene Ontology terms. BMC Bioinformatics. 2004 Sep 2;5:122. PMID: 15345032

Liu Y, Carbonell J, Klein-Seetharaman J, Gopalakrishnan V. Comparison of probabilistic combination methods for protein secondary structure prediction. Bioinformatics. 2004 Nov 22;20(17):3099-107. Epub 2004 Jun 24. PMID: 15217817

Grants

Grant Number: NIGMS K25 GM071951
Project Title: Intelligent Aids for Proteomic Data Mining
Role: Principal Investigator
This project involves the development of machine learning tools to analyze clinical mass spectrometry (MS)-based proteomic data to extract and select features, augment these with heuristic rules and then apply them to a classification test with de-identified patient data. The application of Bayesian methods to improve identification of peptides and proteins using MS-MS spectra will be investigated.

Grant Number: NCI P50 CA0904440 07
Project Title: SPORE in Lung Cancer
Role: Co-director of Biostatistics and Bioinformatics 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.

Lab Personnel

Doctoral students at DBMI:

• Pinaki Mitra
• David Dougall
• Jonathan Lustgarten

Doctoral Students (Thesis Supervisor)

1. Pinaki Mitra (Graduated in 2006;
   ETD: http://etd.library.pitt.edu/ETD/available/etd-08102006-155213/)
2. David Dougall (Graduated in 2007;
   ETD: http://etd.library.pitt.edu/ETD/available/etd-10012007-171912/)
3. Jonathan Lustgarten
4. Philip Ganchev (ISP)
5. Himanshu Grover