A Modularity-Based Method Reveals Mixed Modules from Chemical-Gene Heterogeneous Network
文献类型：期刊论文
| 作者 | Song, Jianglong1; Tang, Shihuan2; Liu, Xi1; Gao, Yibo1 ; Yang, Hongjun2; Lu, Peng1
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| 刊名 | PLOS ONE
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| 出版日期 | 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 |
| 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). |
入库方式： OAI收割
来源：自动化研究所
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