Translational Bioinformatics

Translational bioinformatics is an emerging area in informatics focused on “the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data, and genomic data in particular, into proactive, predictive, preventive, and participatory health” (from the American Medical Informatics Association Strategic Plan http://www.amia.org/inside/stratplan/).  Within DBMI,  Xinghua Lu, MD, PhDVanathi Gopalakrishnan, PhD, Gregory F. Cooper, MD, PhD, Xia Jiang, PhD and Shyam Visweswaran MD, PhD apply machine learning and statistical methods i) for biomarker discovery in high-dimensional ‘omic data such as genomic, transcriptomic and proteomic data, and ii) integration of ‘omic and clinical data for accurate diagnostic, therapeutic, or prognostic predictions.

Sample of Related Publications:

Jin, B, Muller, B, Zhai, CX, and Lu, X (2008) Multi-label literature classification based on the Gene Ontology graph.  BMC Bioinformatics 9:525. PMID: 19063730

Jin, B, Strasburger, A, Laken, SJ, Kozel, FA, Johnson, KA, George, MS, and Lu, X. (2009) Feature selection for fMRI-based deception detectionBMC Bioinformatics 10 (Suppl 9):S15 (Outstanding Paper Award at the AMIA Summit on Translational Bioinformatics 2009)

Lustgarten, JL, Visweswaran, S, Hogan, WR, Gopalakrishnan, V.  Knowledge Based Variable Selection for Learning Rules from Proteomic Data. BMC Bioinformatics. 2009 Sep 17;10 Suppl 9:S16. PMID: 19761570 PMCID: PMC2745687.

Visweswaran, S, Wong, AI, Barmada, MM. A Bayesian method for identifying genetic interactions. In: Proceedings of the Fall Symposium of the American Medical Informatics Association (Nov 2009). 673-7.

Ryberg, H, An, J, Darko, S, Lustgarten, JL, 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

Gopalakrishnan, V, Lustgarten, JL, Visweswaran, S, Cooper, GF. Bayesian Rule Learning for Biomedical Data Mining. Bioinformatics.  26(5) (2010) 668-675. PMID: 20080512; PMCID: PMC2852212

Richards, AJ, Muller, B., Shotwell, M, Cowart, LA, Rohrer, B, and Lu, X. Assessing the functional coherence of gene sets with metrics based on the Gene Ontology graph.  Bioinformatics, supplement issue for the Proceedings for the Intelligent Systems in Molecular Biology (ISMB) 2010 PMID: 20529941

Wang, S, Hauskrecht, M, Visweswaran, S. Candidate gene prioritization using network based probabilistic models. In: Proceedings of the 2010 AMIA Summit on Translational Bioinformatics (Mar 2010).

Jiang, X, Barmada, MM, Visweswaran, S. Identifying genetic Interactions in genome-wide data using Bayesian networks. Genetic Epidemiology. 2010 Sep; 34(6):575-81. PMID: 20568290

Jiang, X, Neapolitan, RE, Barmada, MM, Visweswaran, S, Cooper, GF. A fast algorithm for learning epistatic genomic relationships. In: Proceedings of the Fall Symposium of the American Medical Informatics Association (Nov 2010).

Cooper, GF, Hennings-Yeomans, P, Visweswaran, S, Barmada, MM. An efficient Bayesian method for predicting clinical outcomes from genome-wide data. In: Proceedings of the Fall Symposium of the American Medical Informatics Association (Nov 2010).

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