Computational analysis of muscular dystrophy sub-types using a novel integrative scheme

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• 作者：Chen Wang^a ; ^{topsoil@vt.edu}  ; [Author Vitae] ; Sook Ha^a ; ^sook@vt.edu  ; [Author Vitae] ; Jianhua Xuan^a ; ^xuan@vt.edu  ; [Author Vitae] ; Yue Wang^a ; ^{yuewang@vt.edu}  ; [Author Vitae] ; Eric Hoffman^b ; ^{ehoffman@cnmcresearch.org}  ; [Author Vitae]
• 关键词：Gene expression ; Classification ; Muscular dystrophy ; Affinity propagation clustering ; Biomarker discovery
• 刊名：Neurocomputing
• 出版年：2012
• 期刊代码：95_09252312
• 类别：cp
• 出版时间：1 September, 2012
• 卷：92
• 期：Complete
• 页码：9-17
• 文件大小：959 K

摘要

To construct biologically interpretable gene sets for muscular dystrophy (MD) sub-type classification, we propose a novel computational scheme to integrate protein-protein interaction (PPI) network, functional gene set information, and mRNA profiling data. The workflow of the proposed scheme includes the following three major steps: firstly, we apply an affinity propagation clustering (APC) approach to identify gene sub-networks associated with each MD sub-type, in which a new distance metric is proposed for APC to combine PPI network information and gene-gene co-expression relationship; secondly, we further incorporate functional gene set knowledge, which complements the physical PPI information, into our scheme for biomarker identification; finally, based on the constructed sub-networks and gene set features, we apply multiclass support vector machines (MSVMs) for MD sub-type classification, with which to highlight the biomarkers contributing to sub-type prediction. The experimental results show that our scheme can help identify sub-networks and gene sets that are more relevant to MD than those constructed by other conventional approaches. Moreover, our integrative strategy improves the prediction accuracy substantially, especially for those 鈥榟ard-to-classify鈥?sub-types.