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	Comparison and evaluation of network clustering algorithms applied to genetic interaction networks
	Hou Lin ; Wang Lin ; Berg Arthur ; Qian Minping ; Zhu Yunping ; Li Fangting ; Deng Minghua
	2012
英文摘要	The goal of network clustering algorithms detect dense clusters in a network, and provide a first step towards the understanding of large scale biological networks. With numerous recent advances in biotechnologies, large-scale genetic interactions are widely available, but there is a limited understanding of which clustering algorithms may be most effective. In order to address this problem, we conducted a systematic study to compare and evaluate six clustering algorithms in analyzing genetic interaction networks, and investigated influencing factors in choosing algorithms. The algorithms considered in this comparison include hierarchical clustering, topological overlap matrix, bi-clustering, Markov clustering, Bayesian discriminant analysis based community detection, and variational Bayes approach to modularity. Both experimentally identified and synthetically constructed networks were used in this comparison. The accuracy of the algorithms is measured by the Jaccard index in comparing predicted gene modules with benchmark gene sets. The results suggest that the choice differs according to the network topology and evaluation criteria. Hierarchical clustering showed to be best at predicting protein complexes; Bayesian discriminant analysis based community detection proved best under epistatic miniarray profile (EMAP) datasets; the variational Bayes approach to modularity was noticeably better than the other algorithms in the genome-scale networks.; PubMed; 0; 2150-61; 4
语种	英语
出处	PubMed
出版者	frontiers in bioscience elite edition
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/314173]
专题	数学科学学院
推荐引用方式 GB/T 7714	Hou Lin,Wang Lin,Berg Arthur,et al. Comparison and evaluation of network clustering algorithms applied to genetic interaction networks. 2012-01-01.

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