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

NEUROCOMPUTING

卷:92

Article

Hoffman, Eric¹ 

[1] Childrens Natl Med Ctr, Med Genet Res Ctr, Washington, DC 20010 USA

关键词: Gene expression;    Classification;    Muscular dystrophy;    Affinity propagation clustering;    Biomarker discovery;   

DOI  :  10.1016/j.neucom.2011.08.037

来源: Elsevier

【 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 (PM) 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 subtype 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 'hard-to-classify' sub-types. (c) 2012 Elsevier B.V. All rights reserved.

【 授权许可】

Free