Big data approaches to decomposing heterogeneity across the autism spectrum

Molecular Psychiatry

Volumn 24, Issue 10, 2019, Pages 1435-1450

  Lombardo, Michael V ^a,b   Lai, Meng Chuan ^b,c,d   Baron Cohen, Simon ^b,e  

^a UNIVERSITY OF CYPRUS   (Cyprus)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c UNIVERSITY OF TORONTO   (Canada)

^d NATIONAL TAIWAN UNIVERSITY HOSPITAL   (Taiwan)

^e CAMBRIDGESHIRE AND PETERBOROUGH NHS FOUNDATION TRUST   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AUTISM; BIG DATA; CLINICAL PRACTICE; COGNITION; DIAGNOSTIC AND STATISTICAL MANUAL OF MENTAL DISORDERS; DISEASE CLASSIFICATION; EFFECT SIZE; EVOLUTION; HUMAN; MEDICAL TERMINOLOGY; PREVALENCE; PRIORITY JOURNAL; REVIEW; SAMPLE SIZE; SAMPLING BIAS; STATISTICAL MODEL; SUPERVISED MACHINE LEARNING; UNSUPERVISED MACHINE LEARNING; CASE CONTROL STUDY; CLASSIFICATION; GENETIC HETEROGENEITY; GENETICS; LONGITUDINAL STUDY; PATHOPHYSIOLOGY;

AUTISM SPECTRUM DISORDER; BIG DATA; CASE-CONTROL STUDIES; GENETIC HETEROGENEITY; HUMANS; LONGITUDINAL STUDIES;

EID: 85059695916     PISSN: 13594184     EISSN: 14765578     Source Type: Journal    
DOI: 10.1038/s41380-018-0321-0     Document Type: Review

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