Date of Award

Spring 2010

Document Type

Thesis

Degree Name

Master of Science in Bioinformatics - (M.S.)

Department

Computer Science

First Advisor

Usman W. Roshan

Second Advisor

Jason T. L. Wang

Third Advisor

Zhi Wei

Abstract

This thesis presents an analysis of multiple kernel learning (MKL) for type-1 diabetes risk prediction. MKL combines different models and representation of data to find a linear combination of these representations of the data. MKL has been successfully been implemented in image detection, splice site detection, ribosomal and membrane protein prediction, etc. In this thesis, this method was applied for Genome-wide association study (GWAS) for classifying cases and controls.

This thesis has shown that combined kernel does not perform better than the individual kernels and that MKL does not select the best model for this problem. Also, the effect of normalization on MKL as well as risk prediction has also been analyzed.

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