Open AccessFeature Subset Selection for Cancer Classification Using Weight Local Modularity
Author(s) -
Guodong Zhao,
Yan Wu
Publication year - 2016
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/srep34759
Subject(s) - discriminative model , modularity (biology) , gene selection , feature selection , computer science , pattern recognition (psychology) , predictive power , gene expression profiling , artificial intelligence , selection (genetic algorithm) , computational biology , dna microarray , data mining , microarray analysis techniques , gene , biology , gene expression , genetics , philosophy , epistemology
Microarray is recently becoming an important tool for profiling the global gene expression patterns of tissues. Gene selection is a popular technology for cancer classification that aims to identify a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers to obtain a high predictive accuracy. This technique has been extensively studied in recent years. This study develops a novel feature selection (FS) method for gene subset selection by utilizing the Weight Local Modularity ( WLM ) in a complex network, called the WLMGS . In the proposed method, the discriminative power of gene subset is evaluated by using the weight local modularity of a weighted sample graph in the gene subset where the intra-class distance is small and the inter-class distance is large. A higher local modularity of the gene subset corresponds to a greater discriminative of the gene subset. With the use of forward search strategy, a more informative gene subset as a group can be selected for the classification process. Computational experiments show that the proposed algorithm can select a small subset of the predictive gene as a group while preserving classification accuracy.