SNP interaction detection with Random Forests in high-dimensional genetic data

BMC Bioinformatics

Volumn 13, Issue 1, 2012, Pages

  Winham, Stacey J ^a   Colby, Colin L ^a   Freimuth, Robert R ^a   Wang, Xin ^a   de Andrade, Mariza ^a   Huebner, Marianne ^a,b   Biernacka, Joanna M ^a  

^a MAYO CLINIC   (United States)

^b MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX MODEL; DATA DIMENSIONALITY; DATA-MINING TECHNIQUES; FILTER APPROACH; FILTER TECHNIQUES; GENE-GENE INTERACTION; GENETIC DATA; GENOME-WIDE ASSOCIATION STUDIES; HIGH DIMENSIONAL DATA; HIGH-DIMENSIONAL; HUMAN TRAITS; INTERACTION DETECTION; INTERACTION EFFECT; LOGISTIC REGRESSIONS; LOW DIMENSIONAL; MARGINAL EFFECTS; P-VALUES; PREDICTOR VARIABLES; PROBABILITY OF DETECTION; RANDOM FORESTS; SINGLE-NUCLEOTIDE POLYMORPHISMS; UNIVARIATE; UNIVARIATE ANALYSIS; UNIVARIATE METHOD; VARIABLE IMPORTANCES;

DATA MINING; DECISION TREES; LOGISTICS;

GENES;

ARTICLE; DATA MINING; GENE; GENE LINKAGE DISEQUILIBRIUM; GENETIC ASSOCIATION; HUMAN; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL MODEL;

DATA MINING; GENES; GENOME-WIDE ASSOCIATION STUDY; HUMANS; LINKAGE DISEQUILIBRIUM; LOGISTIC MODELS; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 84867083314     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-13-164     Document Type: Article

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