title: A graph-theoretic approach for identifying non-redundant and relevant gene markers from microarray data using multiobjective binary PSO.
abstract: 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.
authors.affiliation: Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India.
authors.forename: Monalisa
authors.initials: M
authors.lastname: Mandal
authors.affiliation: Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India.
authors.forename: Anirban
authors.initials: A
authors.lastname: Mukhopadhyay
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D005819:Genetic Markers; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D059629:Signal-To-Noise Ratio
chemical_list: D014408:Biomarkers, Tumor; D005819:Genetic Markers