Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique

作者：

Highlights：

• A PSO–adaptive KNN based gene selection method is proposed to select useful genes.

• A heuristic for selecting the optimal values of K efficiently is also proposed.

• The proposed technique is applied on SRBCT, ALL_AML and MLL microarray datasets.

• The usefulness of the identified genes is reconfirmed using SVM classifier.

• The method finds 6, 3 and 4 genes for SRBCT, ALL_AML, and MLL with high accuracy.

摘要

•A PSO–adaptive KNN based gene selection method is proposed to select useful genes.•A heuristic for selecting the optimal values of K efficiently is also proposed.•The proposed technique is applied on SRBCT, ALL_AML and MLL microarray datasets.•The usefulness of the identified genes is reconfirmed using SVM classifier.•The method finds 6, 3 and 4 genes for SRBCT, ALL_AML, and MLL with high accuracy.

论文关键词：Microarray data,SRBCT data,ALL_AML data,MLL data,Particle swarm optimization (PSO),Adaptive K-nearest neighborhood (KNN),Support vector machine (SVM)

论文评审过程：Available online 19 August 2014.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.08.014