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rdf:type |
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lifeskim:mentions |
umls-concept:C0002045,
umls-concept:C0017428,
umls-concept:C0023185,
umls-concept:C0026336,
umls-concept:C0026339,
umls-concept:C0033204,
umls-concept:C0376522,
umls-concept:C0683481,
umls-concept:C0806909,
umls-concept:C1552603,
umls-concept:C1704675,
umls-concept:C1706202,
umls-concept:C1720335,
umls-concept:C1948048,
umls-concept:C2700640
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pubmed:issue |
19
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pubmed:dateCreated |
2009-9-28
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pubmed:abstractText |
In both genome-wide association studies (GWAS) and pathway analysis, the modest sample size relative to the number of genetic markers presents formidable computational, statistical and methodological challenges for accurately identifying markers/interactions and for building phenotype-predictive models.
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pubmed:grant |
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pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/19608708-11404819,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19608708-15793588,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19608708-16369572,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19608708-16457852,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19608708-17048392,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19608708-17082497,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19608708-17529973,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19608708-17586549,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19608708-17721534,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19608708-17971837,
http://linkedlifedata.com/resource/pubmed/commentcorrection/19608708-5073852
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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:month |
Oct
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pubmed:issn |
1367-4811
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pubmed:author |
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pubmed:issnType |
Electronic
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pubmed:day |
1
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pubmed:volume |
25
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
2478-85
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pubmed:dateRevised |
2011-8-1
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pubmed:meshHeading |
pubmed-meshheading:19608708-Algorithms,
pubmed-meshheading:19608708-Computational Biology,
pubmed-meshheading:19608708-Entropy,
pubmed-meshheading:19608708-Genetic Predisposition to Disease,
pubmed-meshheading:19608708-Genome,
pubmed-meshheading:19608708-Genome-Wide Association Study,
pubmed-meshheading:19608708-Models, Statistical,
pubmed-meshheading:19608708-Phenotype,
pubmed-meshheading:19608708-Polymorphism, Single Nucleotide
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pubmed:year |
2009
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pubmed:articleTitle |
An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions.
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pubmed:affiliation |
Department of Electrical Engineering, The Pennsylvania State University, USA. djmiller@engr.psu.edu
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pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't
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