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


Journal

Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837

Informations de publication

Date de publication:
21 05 2019
Historique:
received: 01 08 2017
revised: 24 10 2017
pubmed: 1 12 2017
medline: 3 4 2020
entrez: 30 11 2017
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 29186302
pii: 4662948
doi: 10.1093/bib/bbx157
pmc: PMC6585382
doi:

Substances chimiques

Biomarkers 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

842-856

Subventions

Organisme : NLM NIH HHS
ID : K01 LM012529
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM012095
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM012806
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001117
Pays : United States

Informations de copyright

© The Author 2017. Published by Oxford University Press.

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Auteurs

Jessica D Tenenbaum (JD)

Department of Biostatistics and Bioinformatics at the Duke University School of Medicine.

Krithika Bhuvaneshwar (K)

Innovation Center for Biomedical Informatics (ICBI) at Georgetown University.

Jane P Gagliardi (JP)

Duke University School of Medicine.

Kate Fultz Hollis (K)

Department of Biomedical Informatics and Clinical Epidemiology at Oregon Health and Science University.

Peilin Jia (P)

University of Texas Health Science Center at Houston.

Liang Ma (L)

Bioinformatics and Systems Medicine Laboratory (BSML), Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston.

Radhakrishnan Nagarajan (R)

College of Medicine, University of Kentucky.

Gopalkumar Rakesh (G)

Duke University Medical Center.

Vignesh Subbian (V)

Department of Biomedical Engineering and the Department of Systems and Industrial Engineering at the University of Arizona.

Shyam Visweswaran (S)

University of Pittsburgh.

Zhongming Zhao (Z)

University of Texas Health Science Center at Houston.

Leon Rozenblit (L)

Prometheus Research.

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