dc.contributor.author	Wang, Chen	en
dc.contributor.author	Xuan, Jianhua	en
dc.contributor.author	Li, Huai	en
dc.contributor.author	Wang, Yue	en
dc.contributor.author	Zhan, Ming	en
dc.contributor.author	Hoffman, Eric P.	en
dc.contributor.author	Clarke, Robert	en
dc.contributor.department	Electrical and Computer Engineering	en
dc.date.accessioned	2012-08-01T07:00:18Z	en
dc.date.available	2012-08-01T07:00:18Z	en
dc.date.issued	2010-03-30	en
dc.date.updated	2012-08-01T07:00:18Z	en
dc.description.abstract	Background In cancer, gene networks and pathways often exhibit dynamic behavior, particularly during the process of carcinogenesis. Thus, it is important to prioritize those genes that are strongly associated with the functionality of a network. Traditional statistical methods are often inept to identify biologically relevant member genes, motivating researchers to incorporate biological knowledge into gene ranking methods. However, current integration strategies are often heuristic and fail to incorporate fully the true interplay between biological knowledge and gene expression data. Results To improve knowledge-guided gene ranking, we propose a novel method called coordinative component analysis (COCA) in this paper. COCA explicitly captures those genes within a specific biological context that are likely to be expressed in a coordinative manner. Formulated as an optimization problem to maximize the coordinative effort, COCA is designed to first extract the coordinative components based on a partial guidance from knowledge genes and then rank the genes according to their participation strengths. An embedded bootstrapping procedure is implemented to improve statistical robustness of the solutions. COCA was initially tested on simulation data and then on published gene expression microarray data to demonstrate its improved performance as compared to traditional statistical methods. Finally, the COCA approach has been applied to stem cell data to identify biologically relevant genes in signaling pathways. As a result, the COCA approach uncovers novel pathway members that may shed light into the pathway deregulation in cancers. Conclusion We have developed a new integrative strategy to combine biological knowledge and microarray data for gene ranking. The method utilizes knowledge genes for a guidance to first extract coordinative components, and then rank the genes according to their contribution related to a network or pathway. The experimental results show that such a knowledge-guided strategy can provide context-specific gene ranking with an improved performance in pathway member identification.	en
dc.description.version	Published version	en
dc.format.mimetype	application/pdf	en
dc.identifier.citation	BMC Bioinformatics. 2010 Mar 30;11(1):162	en
dc.identifier.doi	https://doi.org/10.1186/1471-2105-11-162	en
dc.identifier.uri	http://hdl.handle.net/10919/18755	en
dc.language.iso	en	en
dc.rights	Creative Commons Attribution 4.0 International	en
dc.rights.holder	Chen Wang et al.; licensee BioMed Central Ltd.	en
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	en
dc.title	Knowledge-guided gene ranking by coordinative component analysis	en
dc.title.serial	BMC Bioinformatics	en
dc.type	Article - Refereed	en
dc.type.dcmitype	Text	en

Knowledge-guided gene ranking by coordinative component analysis

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