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dc.contributor.authorCosgun, Erdal
dc.contributor.authorLimdi, Nita A.
dc.contributor.authorDuarte, Christine W.
dc.date.accessioned2019-12-10T11:25:04Z
dc.date.available2019-12-10T11:25:04Z
dc.date.issued2011
dc.identifier.issn1367-4803
dc.identifier.urihttps://doi.org/10.1093/bioinformatics/btr159
dc.identifier.urihttp://hdl.handle.net/11655/15690
dc.description.abstractMotivation: With complex traits and diseases having potential genetic contributions of thousands of genetic factors, and with current genotyping arrays consisting of millions of single nucleotide polymorphisms (SNPs), powerful high-dimensional statistical techniques are needed to comprehensively model the genetic variance. Machine learning techniques have many advantages including lack of parametric assumptions, and high power and flexibility. Results: We have applied three machine learning approaches: Random Forest Regression (RFR), Boosted Regression Tree (BRT) and Support Vector Regression (SVR) to the prediction of warfarin maintenance dose in a cohort of African Americans. We have developed a multi-step approach that selects SNPs, builds prediction models with different subsets of selected SNPs along with known associated genetic and environmental variables and tests the discovered models in a cross-validation framework. Preliminary results indicate that our modeling approach gives much higher accuracy than previous models for warfarin dose prediction. A model size of 200 SNPs (in addition to the known genetic and environmental variables) gives the best accuracy. The R-2 between the predicted and actual square root of warfarin dose in this model was on average 66.4% for RFR, 57.8% for SVR and 56.9% for BRT. Thus RFR had the best accuracy, but all three techniques achieved better performance than the current published R-2 of 43% in a sample of mixed ethnicity, and 27% in an African American sample. In summary, machine learning approaches for high-dimensional pharmacogenetic prediction, and for prediction of clinical continuous traits of interest, hold great promise and warrant further research.
dc.language.isoen
dc.publisherOxford Univ Press
dc.relation.isversionof10.1093/bioinformatics/btr159
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBiochemistry & Molecular Biology
dc.subjectBiotechnology & Applied Microbiology
dc.subjectComputer Science
dc.subjectMathematical & Computational Biology
dc.subjectMathematics
dc.titleHigh-Dimensional Pharmacogenetic Prediction Of A Continuous Trait Using Machine Learning Techniques With Application To Warfarin Dose Prediction In African Americans
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.relation.journalBioinformatics
dc.contributor.departmentNöroloji
dc.identifier.volume27
dc.identifier.issue10
dc.identifier.startpage1384
dc.identifier.endpage1389
dc.description.indexWoS


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