Translational bioinformatics in mental health: Open access data sources and computational biomarker discovery

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

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

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.

Original languageEnglish (US)
Article numberbbx157
Pages (from-to)842-856
Number of pages15
JournalBriefings in bioinformatics
Volume20
Issue number3
DOIs
StatePublished - Nov 2 2017

Keywords

  • biomarker discovery
  • mental health
  • open access
  • translational bioinformatics

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology

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