SNP Barcode for Breast Cancer Prediction using Catfish Particle Swarm Optimization

Cheng-Huei Yang, Yu-Da Lin, Li-Yeh Chuang, and Cheng-Hong Yang


Catfish effect, Particle swarm optimization, SNP-SNP interaction, Disease analysis


The analysis of disease-associated polymorphisms by genome-wide association (GWAS) is currently challenging for detection of influences upon their interaction with other genetic factors. Surmising that a gene can be examined in isolation without allowing for potential interactions with other unknown factors might miss the detection of these influences. However, SNP barcodes can be applied for disease prediction and allow the detection of these influences. A particle swarm optimization (PSO) algorithm is introduced and applied to the search of the available SNP barcodes. However, a simple PSO does not assure that every implemented result is indeed a good result. In this study, we use a Catfish particle swarm optimization (CatfishPSO) method for the SNP barcodes of a disease prediction study; breast cancer data was used to test and compare the two method abilities. The experiment demonstrates that the CatfishPSO method is robust and provides exact identification of the best protective SNP barcode for breast cancer.

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