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Breast Cancer Risk Prediction Using Ions Motion Optimization Algorithm

Cheng-Hong Yang 1, Kuo-Chuan Wu 2, 3, and Li-Yeh Chuang 4
1. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan 80708
2. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan 80708
3. Department of Computer Science and Information Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan 80708
4. Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan 80041

Abstract—Breast cancer is the most common female malignancy in the world and has recently increased markedly in Taiwan. In epidemiological studies, several high-penetrance breast cancer genes, such as BRCA1 and BRCA2, were discovered to increase breast cancer risk up to 20-fold. Recently, there are more than 70 Single Nucleotide Polymorphisms (SNPs) identified that influence breast cancer risk in genome-wide association studies (GWAS). This study collects 554 samples of breast cancer and non-breast cancer data, and implements a wrapper algorithm of attribute selection using ions motion optimization (IMO) to identify the SNP-SNP interaction. The experimental results have shown our proposed method has reasonable power to identify SNPs interactions for multi-locus interaction associated with a common complex multifactorial disease.

Index Terms—breast cancer, single nucleotide polymorphisms, SNP-SNP interaction, ions motion optimization

Cite: Cheng-Hong Yang, Kuo-Chuan Wu, and Li-Yeh Chuang, "Breast Cancer Risk Prediction Using Ions Motion Optimization Algorithm," Journal of Life Sciences and Technologies, vol. 4, no. 2, pp. 49-55, December 2016. doi: 10.18178/jolst.4.2.49-55
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