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rdf:type |
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lifeskim:mentions |
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pubmed:issue |
2
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pubmed:dateCreated |
2005-8-2
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pubmed:abstractText |
Non-linear relations between multiple biochemical parameters are the basis for the diagnosis of many diseases. Traditional linear analytical methods are not reliable predictors. Novel nonlinear techniques are increasingly used to improve the diagnostic accuracy of automated data interpretation. This has been exemplified in particular for the classification and diagnostic prediction of cancers based on expression profiling data. Our objective was to predict the genotype from complex biochemical data by comparing the performance of experienced clinicians to traditional linear analysis, and to novel non-linear analytical methods.
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pubmed:grant |
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pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-11518967,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-12930931,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-1309366,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-18267757,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-1964539,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-4815171,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-7475607,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-7564791,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-7588399,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-7629224,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-8136301,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-8154853,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-8531540,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-8968761,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-9207613,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-9385370,
http://linkedlifedata.com/resource/pubmed/commentcorrection/16061837-9888552
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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:chemical |
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pubmed:status |
MEDLINE
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pubmed:month |
Aug
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pubmed:issn |
0804-4643
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pubmed:author |
pubmed-author:BettendorfMarkusM,
pubmed-author:BrabantGeorgG,
pubmed-author:EckertOlafO,
pubmed-author:GroteArnoA,
pubmed-author:Maser-GluthChristianeC,
pubmed-author:NattkemperTim WTW,
pubmed-author:PennerErikaE,
pubmed-author:PrankKlausK,
pubmed-author:SchulzeEgbertE,
pubmed-author:SejnowskiTerrence JTJ,
pubmed-author:von Zur MühlenAlexanderA
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pubmed:issnType |
Print
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pubmed:volume |
153
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
301-5
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pubmed:dateRevised |
2010-9-20
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pubmed:meshHeading |
pubmed-meshheading:16061837-Adult,
pubmed-meshheading:16061837-Artificial Intelligence,
pubmed-meshheading:16061837-Chromosome Mapping,
pubmed-meshheading:16061837-Genotype,
pubmed-meshheading:16061837-Heterozygote,
pubmed-meshheading:16061837-Humans,
pubmed-meshheading:16061837-Linear Models,
pubmed-meshheading:16061837-Middle Aged,
pubmed-meshheading:16061837-Models, Genetic,
pubmed-meshheading:16061837-Mutation,
pubmed-meshheading:16061837-Nonlinear Dynamics,
pubmed-meshheading:16061837-Phenotype,
pubmed-meshheading:16061837-Predictive Value of Tests,
pubmed-meshheading:16061837-Steroid 21-Hydroxylase,
pubmed-meshheading:16061837-Steroids
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pubmed:year |
2005
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pubmed:articleTitle |
Machine learning approaches for phenotype-genotype mapping: predicting heterozygous mutations in the CYP21B gene from steroid profiles.
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pubmed:affiliation |
International NRW Graduate School in Bioinformatics and Genome Research Center of Biotechnology (CeBiTec), Bielefeld University, Germany. klaus.prank@cebitec.uni-bielefeld.de
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pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't
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