A Graph-Theoretic Approach for Identifying Non-Redundant and Relevant Gene Markers from Microarray Data Using Multiobjective Binary PSO

• Published in: PloS one Vol. 9; no. 3; p. e90949
• Main Authors: Mandal, Monalisa, Mukhopadhyay, Anirban
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.03.2014; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology; Biomarkers, Tumor - genetics; Computer Science; DNA microarrays; Engineering; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; Genes; Genetic Markers; Genetic research; Graph theory; Humans; Mathematics; Medicine; Models, Statistical; Models, Theoretical; Multiple objective analysis; Oligonucleotide Array Sequence Analysis - methods; Optimization; Optimization theory; Particle swarm optimization; Prostate cancer; Redundancy; Reproducibility of Results; Signal-To-Noise Ratio; Swarm intelligence
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0090949

The purpose of feature selection is to identify the relevant and non-redundant features from a dataset. In this article, the feature selection problem is organized as a graph-theoretic problem where a feature-dissimilarity graph is shaped from the data matrix. The nodes represent features and the edges represent their dissimilarity. Both nodes and edges are given weight according to the feature's relevance and dissimilarity among the features, respectively. The problem of finding relevant and non-redundant features is then mapped into densest subgraph finding problem. We have proposed a multiobjective particle swarm optimization (PSO)-based algorithm that optimizes average node-weight and average edge-weight of the candidate subgraph simultaneously. The proposed algorithm is applied for identifying relevant and non-redundant disease-related genes from microarray gene expression data. The performance of the proposed method is compared with that of several other existing feature selection techniques on different real-life microarray gene expression datasets.