Bayesian neural networks for detecting epistasis in genetic association studies

BMC Bioinformatics

Volumn 15, Issue 1, 2014, Pages

  Beam, Andrew L ^a   Motsinger Reif, Alison ^b   Doyle, Jon ^c  

^a Harvard Medical School Center for Biomedical Informatics   (United States)

^b NORTH CAROLINA STATE UNIVERSITY   (United States)

^c North Carolina State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER GRAPHICS; GENES; NEURAL NETWORKS; PROGRAM PROCESSORS;

BAYESIAN NEURAL NETWORKS; COMPUTATIONALLY EFFICIENT; GENE-GENE INTERACTION; GENETIC RELATIONSHIPS; GRAPHICS PROCESSING UNITS; NON-PARAMETRIC BAYESIAN; ORDERS OF MAGNITUDE; SYNTHETIC AND REAL DATA;

BAYESIAN NETWORKS;

ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; BIOLOGICAL MODEL; BIOLOGY; CASE CONTROL STUDY; COMPARATIVE STUDY; EPISTASIS; GENETIC ASSOCIATION; GENETICS; HUMAN; MICROBIOLOGY; MYCOBACTERIUM; PATHOGENICITY; PROCEDURES; SINGLE NUCLEOTIDE POLYMORPHISM; TUBERCULOSIS;

BAYES THEOREM; CASE-CONTROL STUDIES; COMPUTATIONAL BIOLOGY; EPISTASIS, GENETIC; GENETIC ASSOCIATION STUDIES; HUMANS; MODELS, GENETIC; MYCOBACTERIUM; NEURAL NETWORKS (COMPUTER); POLYMORPHISM, SINGLE NUCLEOTIDE; TUBERCULOSIS;

EID: 84923857700     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-014-0368-0     Document Type: Article

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