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Current Bioinformatics


ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

General Research Article

Gene Selection Method for Microarray Data Classification Using Particle Swarm Optimization and Neighborhood Rough Set

Author(s): Mingquan Ye*, Weiwei Wang, Chuanwen Yao, Rong Fan and Peipei Wang

Volume 14 , Issue 5 , 2019

Page: [422 - 431] Pages: 10

DOI: 10.2174/1574893614666190204150918

Price: $65


Background: Mining knowledge from microarray data is one of the popular research topics in biomedical informatics. Gene selection is a significant research trend in biomedical data mining, since the accuracy of tumor identification heavily relies on the genes biologically relevant to the identified problems.

Objective: In order to select a small subset of informative genes from numerous genes for tumor identification, various computational intelligence methods were presented. However, due to the high data dimensions, small sample size, and the inherent noise available, many computational methods confront challenges in selecting small gene subset.

Methods: In our study, we propose a novel algorithm PSONRS_KNN for gene selection based on the particle swarm optimization (PSO) algorithm along with the neighborhood rough set (NRS) reduction model and the K-nearest neighborhood (KNN) classifier.

Results: First, the top-ranked candidate genes are obtained by the GainRatioAttributeEval preselection algorithm in WEKA. Then, the minimum possible meaningful set of genes is selected by combining PSO with NRS and KNN classifier.

Conclusion: Experimental results on five microarray gene expression datasets demonstrate that the performance of the proposed method is better than existing state-of-the-art methods in terms of classification accuracy and the number of selected genes.

Keywords: Gene expression data, gene selection, tumor classification, particle swarm optimization, neighborhood rough set, k-nearest neighborhood.

Graphical Abstract
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