An application of Random Forests to a genome-wide association dataset: Methodological considerations and new findings

BMC Genetics

Volumn 11, Issue , 2010, Pages

  Goldstein, Benjamin A ^a   Hubbard, Alan E ^a   Cutler, Adele ^b   Barcellos, Lisa F ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UTAH STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INTERLEUKIN 7;

ALGORITHM; ARTICLE; CASE CONTROL STUDY; COMPUTER ANALYSIS; CONTROLLED STUDY; DISEASE ASSOCIATION; EMPIRICAL RESEARCH; FEASIBILITY STUDY; GENE IDENTIFICATION; GENE LINKAGE DISEQUILIBRIUM; GENE LOCUS; GENETIC ASSOCIATION; GENETIC RISK; GENETIC VARIABILITY; GENOME ANALYSIS; GENOTYPE; HUMAN; MACHINE LEARNING; MAJOR CLINICAL STUDY; MULTIPLE SCLEROSIS; RANDOM FOREST; RELIABILITY; RISK ASSESSMENT; SINGLE NUCLEOTIDE POLYMORPHISM; ARTIFICIAL INTELLIGENCE; BIOLOGY; GENETIC PREDISPOSITION; GENETICS; METHODOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; FEASIBILITY STUDIES; GENETIC PREDISPOSITION TO DISEASE; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; HUMANS; MULTIPLE SCLEROSIS; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 77953351710     PISSN: None     EISSN: 14712156     Source Type: Journal    
DOI: 10.1186/1471-2156-11-49     Document Type: Article

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