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A Modularity-Based Method Reveals Mixed Modules from Chemical-Gene Heterogeneous Network
Song, Jianglong1; Tang, Shihuan2; Liu, Xi1; Gao, Yibo1; Yang, Hongjun2; Lu, Peng1
Source PublicationPLOS ONE
2015-04-30
Volume10Issue:4
SubtypeArticle
AbstractFor 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 HeadingsScience & Technology
WOS KeywordCOMMUNITY STRUCTURE ; HERBAL FORMULAS ; PHARMACOLOGY ; ORGANIZATION ; MEDICINE ; DATABASE ; BIOLOGY ; SYSTEMS
Indexed BySCI
Language英语
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000353713100103
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8076
Collection综合信息系统研究中心
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.China Acad Chinese Med Sci, Inst Chinese Mat Med, Beijing, Peoples R China
Recommended Citation
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).
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