Mining Frequent Patterns for Genetic Variants Associated to Diabetes

Data mining consists of crucial tasks in discovering knowledge and hidden patterns and the tasks are significant in the various areas, such as marketing, biomedical, drugs design, event sequences and etc. Frequent pattern mining is a method that has been explored by a lot of researches in discovering new or hidden knowledge. Therefore, this research attempts to see whether frequent pattern mining method could produce significant information from genetic variants by mining deoxyribonucleic acid (DNA) in particular Single Nucleotide Polymorphism (SNP). The experiments were done using sample enumeration algorithm on diabetes data. Based on our experiments, the genetic variants with diabetes risks were found in low support value. The patterns generated were informative to draw relations between the reported risky SNPs with other unreported SNPs.