RWHMDA: Random Walk on Hypergraph for Microbe-Disease Association Prediction
文献类型：期刊论文
| 作者 | Niu, Ya-Wei1; Qu, Cun-Quan1,3; Wang, Guang-Hui1,3; Yan, Gui-Ying2
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| 刊名 | FRONTIERS IN MICROBIOLOGY
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| 出版日期 | 2019-07-10 |
| 卷号 | 10页码:10 |
| 关键词 | hypergraph random walk microbe human diseases association prediction |
| ISSN号 | 1664-302X |
| DOI | 10.3389/fmicb.2019.01578 |
| 英文摘要 | Based on advancements in deep sequencing technology and microbiology, increasing evidence indicates that microbes inhabiting humans modulate various host physiological phenomena, thus participating in various disease pathogeneses. Owing to increasing availability of biological data, further studies on the establishment of efficient computational models for predicting potential associations are required. In particular, computational approaches can also reduce the discovery cycle of novel microbe-disease associations and further facilitate disease treatment, drug design, and other scientific activities. This study aimed to develop a model based on the random walk on hypergraph for microbe-disease association prediction (RWHMDA). As a class of higher-order data representation, hypergraph could effectively recover information loss occurring in the normal graph methodology, thus exclusively illustrating multiple pair-wise associations. Integrating known microbe-disease associations in the Human Microbe-Disease Association Database (HMDAD) and the Gaussian interaction profile kernel similarity for microbes, random walk was then implemented for the constructed hypergraph. Consequently, RWHMDA performed optimally in predicting the underlying disease-associated microbes. More specifically, our model displayed AUC values of 0.8898 and 0.8524 in global and local leave-one-out cross-validation (LOOCV), respectively. Furthermore, three human diseases (asthma, Crohn's disease, and type 2 diabetes) were studied to further illustrate prediction performance. Moreover, 8, 10, and 8 of the 10 highest ranked microbes were confirmed through recent experimental or clinical studies. In conclusion, RWHMDA is expected to display promising potential to predict disease-microbe associations for follow-up experimental studies and facilitate the prevention, diagnosis, treatment, and prognosis of complex human diseases. |
| 资助项目 | National Natural Science Foundation of China[11631014] |
| WOS研究方向 | Microbiology |
| 语种 | 英语 |
| WOS记录号 | WOS:000474751500002 |
| 出版者 | FRONTIERS MEDIA SA |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/35132]  |
| 专题 | 应用数学研究所 |
| 通讯作者 | Wang, Guang-Hui |
| 作者单位 | 1.Shandong Univ, Sch Math, Jinan, Shandong, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 3.Shandong Univ, Data Sci Inst, Jinan, Shandong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Niu, Ya-Wei,Qu, Cun-Quan,Wang, Guang-Hui,et al. RWHMDA: Random Walk on Hypergraph for Microbe-Disease Association Prediction[J]. FRONTIERS IN MICROBIOLOGY,2019,10:10. |
| APA | Niu, Ya-Wei,Qu, Cun-Quan,Wang, Guang-Hui,&Yan, Gui-Ying.(2019).RWHMDA: Random Walk on Hypergraph for Microbe-Disease Association Prediction.FRONTIERS IN MICROBIOLOGY,10,10. |
| MLA | Niu, Ya-Wei,et al."RWHMDA: Random Walk on Hypergraph for Microbe-Disease Association Prediction".FRONTIERS IN MICROBIOLOGY 10(2019):10. |
入库方式： OAI收割
来源：数学与系统科学研究院
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