Ranking single nucleotide polymorphisms with support vector regression in continuous phenotypes
Department of Mathematical Sciences
Master of Science
Roshan, Usman W.
Dhar, Sunil Kumar
Support vector machines
Single nucleotide polymorphisms
Support vector machines (SVM) have been used to improve the ranking of single nucleotide polymorphisms (SNPs) over traditional chi-square tests in disease case studies . In this investigation, ranking SNPs with support vector regression (SVR) was compared to the Wald test in predicting continuous phenotypes. SVR-ranked SNPs consistently outperformed the Wald test-ranked SNPs to provide a more accurate prediction of the phenotype with fewer SNPs across several methods of prediction.
njit-etd2011-082 (59 pages ~ 6,976 KB pdf)
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Created August 18, 2011