Paper Info
Title
Identifying Non-Redundant Gene Markers from Microarray Data: A Multiobjective Variable Length PSO-Based Approach
Abstract
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.
Year
DOI
Venue
2014
10.1109/TCBB.2014.2323065
Computational Biology and Bioinformatics, IEEE/ACM Transactions  
Keywords
Field
DocType
Pareto optimisation,bioinformatics,cancer,genetics,particle swarm optimisation,pattern classification,cancer diagnostic prediction,classification,disease progression,disease risk,disease susceptibility,expression level,gene expression profiling,microarray data set,microarray technology,multiobjective optimization method,multiobjective variable length PSO-based approach,nonredundant gene markers identification,real-life data sets,state-of-the-art techniques,variable length particle swarm optimization,Multiobjective optimization,non-redundant gene marker,pareto optimality,particle swarm optimization
Particle swarm optimization,Data mining,Data set,Computer science,Multi-objective optimization,Microarray analysis techniques,Bioinformatics,Gene chip analysis,Small set,Optimization problem,Gene expression profiling
Journal
Volume
Issue
ISSN
11
6
1545-5963
Citations 
PageRank 
References 
5
0.44
19
Authors
2
Name
Order
Citations
PageRank
Anirban Mukhopadhyay171150.07
Monalisa Mandal2193.49