Feature selection for genetic sequence classification

Bioinformatics
N A ChuzhanovaS Margetts

Abstract

Most of the existing methods for genetic sequence classification are based on a computer search for homologies in nucleotide or amino acid sequences. The standard sequence alignment programs scale very poorly as the number of sequences increases or the degree of sequence identity is <30%. Some new computationally inexpensive methods based on nucleotide or amino acid compositional analysis have been proposed, but prediction results are still unsatisfactory and depend on the features chosen to represent the sequences. In this paper, a feature selection method based on the Gamma (or near-neighbour) test is proposed. If there is a continuous or smooth map from feature space to the classification target values, the Gamma test gives an estimate for the mean-squared error of the classification, despite the fact that one has no a priori knowledge of the smooth mapping. We can search a large space of possible feature combinations for a combination which gives a smallest estimated mean-squared error using a genetic algorithm. The method was used for feature selection and classification of the large subunits of rRNA according to RDP (Ribosomal Database Project) phylogenetic classes. The sequences were represented by dinucleotide frequency...Continue Reading

Citations

Mar 5, 2003·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·D V RajeR N Singh
Aug 28, 2007·Bioinformatics·Yvan SaeysPedro Larrañaga
Apr 12, 2005·Journal of Zhejiang University. Science. B·Chuan-Bo Chen, Tao Li
Jul 2, 2008·Mathematical Biosciences·Jun Wang, Xiaoqi Zheng
Mar 25, 2009·IEEE Transactions on Nanobioscience·Eghbal G MansooriSeraj D Katebi
Jul 3, 2021·Mathematical Biosciences and Engineering : MBE·Yugui JiangYongquan Zhou

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