Ranking single nucleotide polymorphisms with support vector regression in continuous phenotypes

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.

Name thesis:degree_name

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

Discipline thesis:degree_discipline

Mathematical Sciences

Year

2011

Author dc:creator

• Shahidain, Seif

Contributors dc:contributor

• Usman W. Roshan
• Zhi Wei
• Sunil Kumar Dhar

• Support vector machines
• Single nucleotide polymorphisms
• Continuous phenotypes
• Biostatistics
• Computer Sciences

Repository record dc:identifier

https://digitalcommons.njit.edu/theses/95

OAI identifier oai:identifier

oai:digitalcommons.njit.edu:theses-1094