Ensemble gene selection by grouping for microarray data classification

• Published in: Journal of biomedical informatics Vol. 43; no. 1; pp. 81 - 87
• Main Authors: Liu, Huawen, Liu, Lei, Zhang, Huijie
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2010
• Subjects: Algorithms; Artificial Intelligence; Automatic Data Processing; Cell Line, Tumor; Classification; Computational Biology - methods; Computer Simulation; Databases, Genetic - classification; Ensemble learning; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; Gene selection; Humans; Information metric; Markov blanket; Markov Chains; Microarray analysis; Models, Statistical; Oligonucleotide Array Sequence Analysis - methods; Pattern Recognition, Automated - classification; Pattern Recognition, Automated - methods; Reproducibility of Results; Markov blanket; Gene selection; Information metric; Ensemble learning; Classification; Microarray analysis
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2009.08.010

Selecting relevant and discriminative genes for sample classification is a common and critical task in gene expression analysis (e.g. disease diagnostic). It is desirable that gene selection can improve classification performance of learning algorithm effectively. In general, for most gene selection methods widely used in reality, an individual gene subset will be chosen according to its discriminative power. One of deficiencies of individual gene subset is that its contribution to classification purpose is limited. This issue can be alleviated by ensemble gene selection based on random selection to some extend. However, the random one requires an unnecessary large number of candidate gene subsets and its reliability is a problem. In this study, we propose a new ensemble method, called ensemble gene selection by grouping (EGSG), to select multiple gene subsets for the classification purpose. Rather than selecting randomly, our method chooses salient gene subsets from microarray data by virtue of information theory and approximate Markov blanket. The effectiveness and accuracy of our method is validated by experiments on five publicly available microarray data sets. The experimental results show that our ensemble gene selection method has comparable classification performance to other gene selection methods, and is more stable than the random one.