Document Type
Thesis
Date of Award
Spring 5-31-2011
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.
Recommended Citation
Shahidain, Seif, "Ranking single nucleotide polymorphisms with support vector regression in continuous phenotypes" (2011). Theses. 95.
https://digitalcommons.njit.edu/theses/95
