« Previous AbstractExpression patterns and ligand binding characterization of Plus-C odorant-binding protein 14 from Adelphocoris lineolatus (Goeze)    Next Abstract"Ammonium and nitrate sources and transformation mechanism in the Quaternary sediments of Jianghan Plain, China" »

Sci Rep

Title:		A Hybrid Gene Selection Method Based on ReliefF and Ant Colony Optimization Algorithm for Tumor Classification
Author(s):		Sun L; Kong X; Xu J; Xue Z; Zhai R; Zhang S;
Address:		"College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China. linsunok@gmail.com. Post-doctoral Mobile Station of Biology, College of Life Science, Henan Normal University, Xinxiang, China. linsunok@gmail.com. College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China. College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China. jiuchxu@gmail.com. Post-doctoral Mobile Station of Biology, College of Life Science, Henan Normal University, Xinxiang, China. jiuchxu@gmail.com. School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China"
Journal Title:		Sci Rep
Year:		2019
Volume:		20190620
Issue:		1
Page Number:		8978 -
DOI:		10.1038/s41598-019-45223-x
ISSN/ISBN:		2045-2322 (Electronic) 2045-2322 (Linking)
Abstract:		"For the DNA microarray datasets, tumor classification based on gene expression profiles has drawn great attention, and gene selection plays a significant role in improving the classification performance of microarray data. In this study, an effective hybrid gene selection method based on ReliefF and Ant colony optimization (ACO) algorithm for tumor classification is proposed. First, for the ReliefF algorithm, the average distance among k nearest or k non-nearest neighbor samples are introduced to estimate the difference among samples, based on which the distances between the samples in the same class or the different classes are defined, and then it can more effectively evaluate the weight values of genes for samples. To obtain the stable results in emergencies, a distance coefficient is developed to construct a new formula of updating weight coefficient of genes to further reduce the instability during calculations. When decreasing the distance between the same samples and increasing the distance between the different samples, the weight division is more obvious. Thus, the ReliefF algorithm can be improved to reduce the initial dimensionality of gene expression datasets and obtain a candidate gene subset. Second, a new pruning rule is designed to reduce dimensionality and obtain a new candidate subset with the smaller number of genes. The probability formula of the next point in the path selected by the ants is presented to highlight the closeness of the correlation relationship between the reaction variables. To increase the pheromone concentration of important genes, a new phenotype updating formula of the ACO algorithm is adopted to prevent the pheromone left by the ants that are overwhelmed with time, and then the weight coefficients of the genes are applied here to eliminate the interference of difference data as much as possible. It follows that the improved ACO algorithm has the ability of the strong positive feedback, which quickly converges to an optimal solution through the accumulation and the updating of pheromone. Finally, by combining the improved ReliefF algorithm and the improved ACO method, a hybrid filter-wrapper-based gene selection algorithm called as RFACO-GS is proposed. The experimental results under several public gene expression datasets demonstrate that the proposed method is very effective, which can significantly reduce the dimensionality of gene expression datasets, and select the most relevant genes with high classification accuracy"
Keywords:		"*Algorithms *Biomarkers, Tumor Computational Biology/*methods/standards Humans Neoplasms/*diagnosis/*genetics Oligonucleotide Array Sequence Analysis/*methods/standards Oncogene Proteins, Fusion/*genetics Reproducibility of Results;"
Notes:		"MedlineSun, Lin Kong, Xianglin Xu, Jiucheng Xue, Zhan'ao Zhai, Ruibing Zhang, Shiguang eng Research Support, Non-U.S. Gov't England 2019/06/22 Sci Rep. 2019 Jun 20; 9(1):8978. doi: 10.1038/s41598-019-45223-x"