Regression and data mining methods for analyses of multiple rare variants in the Genetic Analysis Workshop 17 mini-exome data

Genetic Epidemiology

Volumn 35, Issue SUPPL. 1, 2011, Pages

  Bailey Wilson, Joan E ^a   Brennan, Jennifer S ^b   Bull, Shelley B ^c,d   Culverhouse, Robert ^e   Kim, Yoonhee ^a   Jiang, Yuan ^b   Jung, Jeesun ^f   Li, Qing ^a   Lamina, Claudia ^g   Liu, Ying ^h   Mägi, Reedik ⁱ   Niu, Yue S ^j   Simpson, Claire L ^a   Wang, Libo ^k   Yilmaz, Yildiz E ^c,d   Zhang, Heping ^b   Zhang, Zhaogong ^l  

^a NATIONAL HUMAN GENOME RESEARCH INSTITUTE   (United States)

^b YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c MOUNT SINAI HOSPITAL   (Canada)

^d UNIVERSITY OF TORONTO   (Canada)

^e WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^f INDIANA UNIVERSITY SCHOOL OF MEDICINE   (United States)

^g INNSBRUCK MEDICAL UNIVERSITY   (Austria)

^h Och Spine at New York Presbyterian Hospitals   (United States)

ⁱ UNIVERSITY OF OXFORD   (United Kingdom)

^j University of Arizona   (United States)

^k PURDUE UNIVERSITY   (United States)

^l MICHIGAN TECHNOLOGICAL UNIVERSITY   (United States)

more..

Author keywords

Binary trees; Classification trees; Extreme sampling; ISIS; LASSO; Logic regression; Machine learning; Meta analysis; Poisson regression; Random forests; Rare variants

Indexed keywords

VASCULOTROPIN A; VASCULOTROPIN RECEPTOR 1;

ARTICLE; DATA MINING; DNA SEQUENCE; EFFECT SIZE; EXOME; FOLLOW UP; GENE FREQUENCY; GENE LOCUS; GENE MUTATION; GENETIC ANALYSIS; GENETIC VARIABILITY; HERITABILITY; HUMAN; MACHINE LEARNING; PHENOTYPE; POPULATION STRUCTURE; REGRESSION ANALYSIS; SIGNAL TRANSDUCTION; SINGLE NUCLEOTIDE POLYMORPHISM;

ARTIFICIAL INTELLIGENCE; DATA INTERPRETATION, STATISTICAL; DATA MINING; EXOME; GENETIC PREDISPOSITION TO DISEASE; GENETIC VARIATION; HUMAN GENOME PROJECT; HUMANS; META-ANALYSIS AS TOPIC; MOLECULAR EPIDEMIOLOGY; POLYMORPHISM, SINGLE NUCLEOTIDE; REGRESSION ANALYSIS; SEQUENCE ANALYSIS;

EID: 82455219111     PISSN: 07410395     EISSN: 10982272     Source Type: Journal    
DOI: 10.1002/gepi.20657     Document Type: Article

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