A Novel Wrapper-filter Hybrid Method for Candidate SNPs Selection
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2016, Vol 18, Issue 4
Abstract
Abstract: Genomic studies provide massive amount of data including thousands of Single Nucleotide Polymorphisms (SNPs). The analysis of SNPs helps to identify genetic variants related to complex traits. Therefore, it is essential to provide an efficient method to find a small subset of candidate SNPs as good representatives of the rest of SNPs.In this study, a new feature/SNP selection method based on the relationship between filter and wrapper criteria (i.e. correlation and model accuracy) is proposed. The method is based on the prediction of Mean Square Error (MSE) in terms of the number of features, Mean Feature-Feature Correlation (MFFC) and Mean Feature-Target Correlation (MFTC). It trains a Neural Network (NN) to predictthe accuracy in terms of the number of features, MFFC and MFTC.We ran experiments on artificial SNPs datasets, comparing our algorithm with well-known feature selection techniques, and obtained higher accuracy in selecting the candidate SNPs in shorter running time.
Authors and Affiliations
Farideh Halakou
Keywords
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