Identifying Non-Redundant Gene Markers from Microarray Data: A Multiobjective Variable Length PSO-Based Approach

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 11; no. 6; pp. 1170 - 1183
• Main Authors: Mukhopadhyay, Anirban, Mandal, Monalisa
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biomarkers; Cancer; Cluster Analysis; Computational Biology - methods; Databases, Genetic; Gene expression; Gene Expression Profiling - methods; Genes; Genetic Markers - genetics; Humans; Markers; Mathematical model; Mathematical models; Medical services; Neoplasms - genetics; Neoplasms - metabolism; Oligonucleotide Array Sequence Analysis - methods; Optimization; Sensitivity and Specificity; Sociology; Statistical analysis; particle swarm optimization; pareto optimality; Multiobjective optimization; non-redundant gene marker
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2014.2323065

Identifying relevant genes which are responsible for various types of cancer is an important problem. In this context, important genes refer to the marker genes which change their expression level in correlation with the risk or progression of a disease, or with the susceptibility of the disease to a given treatment. Gene expression profiling by microarray technology has been successfully applied to classification and diagnostic prediction of cancers. However, extracting these marker genes from a huge set of genes contained by the microarray data set is a major problem. Most of the existing methods for identifying marker genes find a set of genes which may be redundant in nature. Motivated by this, a multiobjective optimization method has been proposed which can find a small set of non-redundant disease related genes providing high sensitivity and specificity simultaneously. In this article, the optimization problem has been modeled as a multiobjective one which is based on the framework of variable length particle swarm optimization. Using some real-life data sets, the performance of the proposed algorithm has been compared with that of other state-of-the-art techniques.