Gene selection for microarray data classification via multi-objective graph theoretic-based method

Highlights

• A novel gene selection method has been developed by integrating the concept of node centrality and community detection.

• The main goal of this method is to select a subset of the genes with lowest similarity and highest dependency.

• In this proposed method the optimal number of final gene set is determined automatically.

• Experimental results showed that the proposed method has the best performance among different gene selection methods.

• The results on five microarray datasets indicate that this method improves microarray data classification accuracy.

Abstract

In recent decades, the improvement of computer technology has increased the growth of high-dimensional microarray data. Thus, data mining methods for DNA microarray data classification usually involve samples consisting of thousands of genes. One of the efficient strategies to solve this problem is gene selection, which improves the accuracy of microarray data classification and also decreases computational complexity. In this paper, a novel social network analysis-based gene selection approach is proposed. The proposed method has two main objectives of the relevance maximization and redundancy minimization of the selected genes. In this method, on each iteration, a maximum community is selected repetitively. Then among the existing genes in this community, the appropriate genes are selected by using the node centrality-based criterion. The reported results indicate that the developed gene selection algorithm while increasing the classification accuracy of microarray data, will also decrease the time complexity.