A Modularity-Based Method Reveals Mixed Modules from Chemical-Gene Heterogeneous Network

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	A Modularity-Based Method Reveals Mixed Modules from Chemical-Gene Heterogeneous Network
	Song, Jianglong 1; Tang, Shihuan 2; Liu, Xi 1; Gao, Yibo1 ; Yang, Hongjun 2; Lu, Peng1
刊名	PLOS ONE
	2015-04-30
卷号	10 期号:4
英文摘要	For a multicomponent therapy, molecular network is essential to uncover its specific mode of action from a holistic perspective. The molecular system of a Traditional Chinese Medicine (TCM) formula can be represented by a 2-class heterogeneous network (2-HN), which typically includes chemical similarities, chemical-target interactions and gene interactions. An important premise of uncovering the molecular mechanism is to identify mixed modules from complex chemical-gene heterogeneous network of a TCM formula. We thus proposed a novel method (MixMod) based on mixed modularity to detect accurate mixed modules from 2-HNs. At first, we compared MixMod with Clauset-Newman-Moore algorithm (CNM), Markov Cluster algorithm (MCL), Infomap and Louvain on benchmark 2-HNs with known module structure. Results showed that MixMod was superior to other methods when 2-HNs had promiscuous module structure. Then these methods were tested on a real drug-target network, in which 88 disease clusters were regarded as real modules. MixMod could identify the most accurate mixed modules from the drug-target 2-HN (normalized mutual information 0.62 and classification accuracy 0.4524). In the end, MixMod was applied to the 2-HN of Buchang naoxintong capsule (BNC) and detected 49 mixed modules. By using enrichment analysis, we investigated five mixed modules that contained primary constituents of BNC intestinal absorption liquid. As a matter of fact, the findings of in vitro experiments using BNC intestinal absorption liquid were found to highly accord with previous analysis. Therefore, MixMod is an effective method to detect accurate mixed modules from chemical-gene heterogeneous networks and further uncover the molecular mechanism of multicomponent therapies, especially TCM formulae.
WOS标题词	Science & Technology
类目[WOS]	Multidisciplinary Sciences
研究领域[WOS]	Science & Technology - Other Topics
关键词[WOS]	COMMUNITY STRUCTURE ; HERBAL FORMULAS ; PHARMACOLOGY ; ORGANIZATION ; MEDICINE ; DATABASE ; BIOLOGY ; SYSTEMS
收录类别	SCI
语种	英语
WOS记录号	WOS:000353713100103
公开日期	2015-09-22
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/8076]
专题	自动化研究所_综合信息系统研究中心
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 2.China Acad Chinese Med Sci, Inst Chinese Mat Med, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Song, Jianglong,Tang, Shihuan,Liu, Xi,et al. A Modularity-Based Method Reveals Mixed Modules from Chemical-Gene Heterogeneous Network[J]. PLOS ONE,2015,10(4).
APA	Song, Jianglong,Tang, Shihuan,Liu, Xi,Gao, Yibo,Yang, Hongjun,&Lu, Peng.(2015).A Modularity-Based Method Reveals Mixed Modules from Chemical-Gene Heterogeneous Network.PLOS ONE,10(4).
MLA	Song, Jianglong,et al."A Modularity-Based Method Reveals Mixed Modules from Chemical-Gene Heterogeneous Network".PLOS ONE 10.4(2015).