Translational bioinformatics is 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/ ).
Faculty in DBMI are active in a wide range of research projects in translational bioinformatics, with several projects focused on precision medicine. Examples of projects include: (1) computational methods for predicting protein-protein interactions in the brain; (2) pattern-mining sequence analysis in whole genome and whole-proteome sequences; (3) algorithms for detecting epistasis – the interaction between two or more genes to affect phenotype – from high-dimensional genomic data; (4) personalized models that identify genomic factors specific to individuals; (5) deep mining of a comprehensive genomic compendium to decipher cellular signal transduction systems; and (6) methods for causal discovery and inference from biomedical and clinical datasets.
Vukmirovic M, Yan X, Gibson KF, Gulati M, Schupp JC, DeIuliis G, Adams TS, Hu B, Mihaljinec A, Woolard TN, Lynn H, Emeagwali N, Herzog EL, Chen ES, Morris A, Leader JK, Zhang Y, Garcia JGN, Maier LA, Collman RG, Drake WP, Becich MJ, Hochheiser H, Wisniewski SR, Benos PV, Moller DR, Prasse A, Koth LL, Kaminski N; GRADS Investigators. Transcriptomics of bronchoalveolar lavage cells identifies new molecular endotypes of sarcoidosis. Eur Respir J. 2021 Dec 2;58(6):2002950. doi: 10.1183/13993003.02950-2020. PMID: 34083402.
Karunakaran KB, Yanamala N, Boyce G, Becich MJ, Ganapathiraju MK. Malignant Pleural Mesothelioma Interactome with 364 Novel Protein-Protein Interactions. Cancers (Basel). 2021 Apr 1;13(7):1660. doi: 10.3390/cancers13071660. PMID: 33916178; PMCID: PMC8037232.
Cooley NP, Wright ES. Accurate annotation of protein coding sequences with IDTAXA. NAR Genom Bioinform. 2021 Sep 16;3(3):lqab080. doi: 10.1093/nargab/lqab080. PMID: 34541527; PMCID: PMC8445202.
Jiang X, Wells A, Brufsky A, Shetty D, Shajihan K, Neapolitan RE. Leveraging Bayesian networks and information theory to learn risk factors for breast cancer metastasis. BMC Bioinformatics. 2020 Jul 10;21(1):298. doi: 10.1186/s12859-020-03638-8. PMID: 32650714; PMCID: PMC7350636.
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):98-109. doi: 10.5306/wjco.v9.i5.98. PMID: 30254965; PMCID: PMC6153126.