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rdf:type |
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lifeskim:mentions |
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pubmed:dateCreated |
2010-7-5
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pubmed:abstractText |
As computational power improves, the application of more advanced machine learning techniques to the analysis of large genome-wide association (GWA) datasets becomes possible. While most traditional statistical methods can only elucidate main effects of genetic variants on risk for disease, certain machine learning approaches are particularly suited to discover higher order and non-linear effects. One such approach is the Random Forests (RF) algorithm. The use of RF for SNP discovery related to human disease has grown in recent years; however, most work has focused on small datasets or simulation studies which are limited.
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pubmed:grant |
http://linkedlifedata.com/resource/pubmed/grant/076113,
http://linkedlifedata.com/resource/pubmed/grant/G0700061,
http://linkedlifedata.com/resource/pubmed/grant/MH 63420,
http://linkedlifedata.com/resource/pubmed/grant/MH059565,
http://linkedlifedata.com/resource/pubmed/grant/MH059571,
http://linkedlifedata.com/resource/pubmed/grant/MH059588,
http://linkedlifedata.com/resource/pubmed/grant/MH067257,
http://linkedlifedata.com/resource/pubmed/grant/MH59566,
http://linkedlifedata.com/resource/pubmed/grant/MH59586,
http://linkedlifedata.com/resource/pubmed/grant/MH59587,
http://linkedlifedata.com/resource/pubmed/grant/MH60870,
http://linkedlifedata.com/resource/pubmed/grant/NS032830,
http://linkedlifedata.com/resource/pubmed/grant/R01 NS049477,
http://linkedlifedata.com/resource/pubmed/grant/R01AI076544,
http://linkedlifedata.com/resource/pubmed/grant/R01NS049510,
http://linkedlifedata.com/resource/pubmed/grant/T32 HG 00047,
http://linkedlifedata.com/resource/pubmed/grant/U01 MH060879
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pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-12747598,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-15532037,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-15588316,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-15593090,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-15973459,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-16398926,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-16595076,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-16630340,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-17254353,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-17554300,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-17554624,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-17660530,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-17701901,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-17924348,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-18163421,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-18349094,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-18466554,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-18466563,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-18466642,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-19014573,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-19265542,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-19429005,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-19525953,
http://linkedlifedata.com/resource/pubmed/commentcorrection/20546594-19525955
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pubmed:language |
eng
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pubmed:journal |
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pubmed:citationSubset |
IM
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pubmed:status |
MEDLINE
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pubmed:issn |
1471-2156
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pubmed:author |
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pubmed:issnType |
Electronic
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pubmed:volume |
11
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
49
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pubmed:dateRevised |
2010-9-28
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pubmed:meshHeading |
pubmed-meshheading:20546594-Algorithms,
pubmed-meshheading:20546594-Artificial Intelligence,
pubmed-meshheading:20546594-Computational Biology,
pubmed-meshheading:20546594-Feasibility Studies,
pubmed-meshheading:20546594-Genetic Predisposition to Disease,
pubmed-meshheading:20546594-Genome-Wide Association Study,
pubmed-meshheading:20546594-Genotype,
pubmed-meshheading:20546594-Humans,
pubmed-meshheading:20546594-Multiple Sclerosis,
pubmed-meshheading:20546594-Polymorphism, Single Nucleotide
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pubmed:year |
2010
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pubmed:articleTitle |
An application of Random Forests to a genome-wide association dataset: methodological considerations & new findings.
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pubmed:affiliation |
Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA. bgoldstein@genepi.berkeley.edu
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pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't,
Research Support, N.I.H., Extramural
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