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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol. 4(5): 468-473 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.456

Determination of the SNP-SNP Interaction between Breast Cancer Related Genes to Analyze the Disease Susceptibility

Mei-Lee Hwang, Yu-Da Lin, Li-Yeh Chuang, and Cheng-Hong Yang
Abstract—Investigation of the single nucleotide polymorphism (SNP)-SNP interaction model can facilitate the analysis of the susceptibility to disease. The model explains the risk of association between the genotypes and the disease in case-control study. Thus, many mathematic methods are widely applied to identify the statistically significant model such as odds ratio (OR), chi-square test, and error rate. However, a huge number of data sets have been found to limit the statistical methods to identify the significant model. In this study, we propose a novel statistical method, complementary-logic particle swarm optimization (CLPSO), to increase the efficiency of significant model identification in case-control study. The complementary-logic is implemented to improve the PSO search ability and identify a better SNP-SNP interaction model. Six important breast cancer genes including 23 SNPs and simulated huge number of data sets were selected as the test data sets. The methods of PSO and CLPSO were applied on the identification of SNP-SNP interactions in the two-way to five-way. In results, the OR evaluates the breast cancer risk of the identified SNP-SNP interaction model. Compared to the corresponding non-interaction model, if the OR value is greater than 1 that indicates the model is significant risk between cases and controls. The results showed that CLPSO is able to identify the significant models for specific SNP-SNP interaction of two-way to five-way (OR value: 1.153-1.391; confidence interval (CI): 1.05-1.79; p-value: 0.01-0.003). The model suggests that the genes ESR1, PGR, and SHBG may be an important role in the interactive effects to breast cancer. In addition, we compared the search abilities of PSO and CLPSO for identification of the significant model. Results revealed that CLPSO can identify better model with difference values between cases and controls than PSO; it suggests CLPSO can be used to identify a better SNP-SNP interaction models.

Index Terms—Single nucleotide polymorphism (SNP), particle swarm optimization (PSO), breast cancer.

Mei-Lee Hwang and Li-Yeh Chuang are with the Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan (e-mail: mlhwang@isu.edu.tw, chuang@isu.edu.tw).
Yu-Da Lin and Cheng-Hong Yang are with the Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan (e-mail: e0955767257@yahoo.com.tw, chyang@cc.kuas.edu.tw).

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Cite: Mei-Lee Hwang, Yu-Da Lin, Li-Yeh Chuang, and Cheng-Hong Yang, "Determination of the SNP-SNP Interaction between Breast Cancer Related Genes to Analyze the Disease Susceptibility," International Journal of Machine Learning and Computing vol. 4, no. 5, pp. 468-473, 2014.

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