Translational bioinformatics in mental health: open access data sources and computational biomarker discovery
Tenenbaum JD, Bhuvaneshwar K, Gagliardi JP, Fultz Hollis K, Jia P, Ma L, Nagarajan R, Rakesh G, Subbian V, Visweswaran S, Zhao Z, Rozenblit L. Translational bioinformatics in mental health: open access data sources and computational biomarker discovery. Brief Bioinform. 2017 Nov 27. doi: 10.1093/bib/bbx157. [Epub ahead of print] PMID: 29186302
Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probemolecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic,metabolomic, proteomic, clinical and imaging resources. General data repositories such as the Gene Expression Omnibus (GEO) and Database of Genotypes and Phenotypes (dbGaP) andmental health (MH)-specific initiatives, such as the Psychiatric Genomics Consortium, MH Research Network and PsychENCODE represent a wealth of information yet to be gleaned. At the same time, novel approaches to integrate and analyze data sets are enabling important discoveries in the area ofmental and behavioral health. This review will discuss and catalog into an organizing framework the increasingly diverse set of MH data resources available, using schizophrenia as a focus area, and will describe novel and integrative approaches tomolecular biomarker discovery thatmake use of mental health data.