RWHMDA: Random Walk on Hypergraph for Microbe-Disease Association Prediction

doi:10.3389/fmicb.2019.01578

CSpace > 应用数学研究所

	RWHMDA: Random Walk on Hypergraph for Microbe-Disease Association Prediction
	Niu, Ya-Wei 1; Qu, Cun-Quan 1,2; Wang, Guang-Hui 1,2; Yan, Gui-Ying3
	2019-07-10
发表期刊	FRONTIERS IN MICROBIOLOGY
ISSN	1664-302X
卷号	10 页码:10
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.
关键词	hypergraph random walk microbe human diseases association prediction
DOI	10.3389/fmicb.2019.01578
语种	英语
资助项目	National Natural Science Foundation of China[11631014]
WOS研究方向	Microbiology
WOS类目	Microbiology
WOS记录号	WOS:000474751500002
出版者	FRONTIERS MEDIA SA
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/35133
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.Shandong Univ, Data Sci Inst, Jinan, Shandong, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, 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.