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论文类型：期刊论文

发表时间：2015-08-01

发表刊物：MATHEMATICAL BIOSCIENCES

收录刊物：SCIE、EI、PubMed、Scopus

卷号：266

页面范围：65-72

ISSN号：0025-5564

关键字：Biological knowledge; Information fusion; Neighborhood rough set; Plant stress response

摘要：Classification of microarray data has always been a challenging task because of the enormous number of genes. In this study, a clustering method by integrating plant stress response gene expression data with biological knowledge is presented. Clustering is one of the promising tools for attribute reduction, but gene clusters are biologically uninformative. So we integrated biological knowledge into genomic analysis to help to improve the interpretation of the results. Biological similarity based on gene ontology (GO) semantic similarity was combined with gene expression data to find out biologically meaningful clusters. Affinity propagation clustering algorithm was chosen to analyze the impact of the biological similarity on the results. Based on clustering result, neighborhood rough set was used to select representative genes for each cluster. The prediction accuracy of classifiers built on reduced gene subsets indicated that our approach outperformed other classical methods. The information fusion was proven to be effective through quantitative analysis, as it could select gene subsets with high biological significance and select significant genes. (C) 2015 Elsevier Inc. All rights reserved.