Abstract
A common type of DNA variation is called a Single Nucleotide Polymorphism (SNP), where a single position within a DNA sequence is altered from one nucleotide base to another. The problem of identifying disease-associated SNPs has been the subject of extensive research by statisticians. However, less research has been done within the computing community. In this paper, we propose a novel geometrical computing model for the SNP Motif Identification Problem. The purpose of our research is to explore the properties of SNPs in a combinatorial way. We test our algorithm on two real clinical datasets, and give computational results which demonstrate the efficiency and effectiveness of our approach.
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