Machine Learning
Evolution Revolution: An Introduction to the Special Track on Genetic and Evolutionary Programming
IEEE Expert: Intelligent Systems and Their Applications
Haplotypes and informative SNP selection algorithms: don't block out information
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Choosing SNPs Using Feature Selection
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Bioinformatics
SVM Learning from Imbalanced Data by GA Sampling for Protein Domain Prediction
ICYCS '08 Proceedings of the 2008 The 9th International Conference for Young Computer Scientists
Machine Learning in Bioinformatics
Machine Learning in Bioinformatics
A tool for multiobjective evolutionary algorithms
Advances in Engineering Software
Tag SNP selection via a genetic algorithm
Journal of Biomedical Informatics
Expert Systems with Applications: An International Journal
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SNPs (Single Nucleotide Polymorphisms) include millions of changes in human genome, and therefore, are promising tools for disease-gene association studies. However, this kind of studies is constrained by the high expense of genotyping millions of SNPs. For this reason, it is required to obtain a suitable subset of SNPs to accurately represent the rest of SNPs. For this purpose, many methods have been developed to select a convenient subset of tag SNPs, but all of them only provide low prediction accuracy. In the present study, a brand new method is developed and introduced as GA-SVM with parameter optimization. This method benefits from support vector machine (SVM) and genetic algorithm (GA) to predict SNPs and to select tag SNPs, respectively. Furthermore, it also uses particle swarm optimization (PSO) algorithm to optimize C and @c parameters of support vector machine. It is experimentally tested on a wide range of datasets, and the obtained results demonstrate that this method can provide better prediction accuracy in identifying tag SNPs compared to other methods at present.