Date of Award

Spring 2011

Document Type

Thesis

Degree Name

Master of Science in Computational Biology - (M.S.)

Department

Mathematical Sciences

First Advisor

Usman W. Roshan

Second Advisor

Zhi Wei

Third Advisor

Sunil Kumar Dhar

Abstract

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 [2]. 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.

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