Translational bioinformatics in mental health: open access data sources and computational biomarker discovery
AuthorTenenbaum, Jessica D
Gagliardi, Jane P
Fultz Hollis, Kate
AffiliationUniv Arizona, Dept Biomed Engn
MetadataShow full item record
PublisherOXFORD UNIV PRESS
CitationJessica 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, Translational bioinformatics in mental health: open access data sources and computational biomarker discovery, Briefings in Bioinformatics, Volume 20, Issue 3, May 2019, Pages 842–856, https://doi.org/10.1093/bib/bbx157
JournalBRIEFINGS IN BIOINFORMATICS
Rights© The Author 2017. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractMental 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.
NoteOpen access article
VersionFinal published version
SponsorsNational Institutes of Health [UL1TR001117, R01LM012095, R01LM012806]
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