Translational Bioinformatics in Mental Health: Open Access Data Sources and Computational Biomarker Discovery

Overview

Journal Brief Bioinform

Publisher Oxford University Press

Specialty Biology

Date 2017 Nov 30

PMID 29186302

Citations 7

Authors

Jessica D Tenenbaum

Krithika Bhuvaneshwar

Jane P Gagliardi

Kate Fultz Hollis

Peilin Jia

Liang Ma

Radhakrishnan Nagarajan

Gopalkumar Rakesh

Vignesh Subbian

Shyam Visweswaran

Zhongming Zhao

Leon Rozenblit

Affiliations

Soon will be listed here.

Abstract

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 probe molecular 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) and mental 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 of mental 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 to molecular biomarker discovery that make use of mental health data.

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