Prediction of microbe-disease association from the integration of neighbor and graph with collaborative recommendation model

doi:10.1186/s12967-017-1304-7

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	Prediction of microbe-disease association from the integration of neighbor and graph with collaborative recommendation model
	Huang, Yu-An 1; You, Zhu-Hong 1; Chen, Xing 2; Huang, Zhi-An 3; Zhang, Shanwen 1; Yan, Gui-Ying4
	2017-10-16
Source Publication	JOURNAL OF TRANSLATIONAL MEDICINE
ISSN	1479-5876
Volume	15 Pages:11
Abstract	Background: Accumulating clinical researches have shown that specific microbes with abnormal levels are closely associated with the development of various human diseases. Knowledge of microbe-disease associations can provide valuable insights for complex disease mechanism understanding as well as the prevention, diagnosis and treatment of various diseases. However, little effort has been made to predict microbial candidates for human complex diseases on a large scale. Methods: In this work, we developed a new computational model for predicting microbe-disease associations by combining two single recommendation methods. Based on the assumption that functionally similar microbes tend to get involved in the mechanism of similar disease, we adopted neighbor-based collaborative filtering and a graphbased scoring method to compute association possibility of microbe-disease pairs. The promising prediction performance could be attributed to the use of hybrid approach based on two single recommendation methods as well as the introduction of Gaussian kernel-based similarity and symptom-based disease similarity. Results: To evaluate the performance of the proposed model, we implemented leave-one-out and fivefold cross validations on the HMDAD database, which is recently built as the first database collecting experimentally-confirmed microbe-disease associations. As a result, NGRHMDA achieved reliable results with AUCs of 0.9023 +/- 0.0031 and 0.9111 in the validation frameworks of fivefold CV and LOOCV. In addition, 78.2% microbe samples and 66.7% disease samples are found to be consistent with the basic assumption of our work that microbes tend to get involved in the similar disease clusters, and vice versa. Conclusions: Compared with other methods, the prediction results yielded by NGRHMDA demonstrate its effective prediction performance for microbe-disease associations. It is anticipated that NGRHMDA can be used as a useful tool to search the most potential microbial candidates for various diseases, and therefore boosts the medical knowledge and drug development. The codes and dataset of our work can be downloaded from https://github.com/ yahuang1991/NGRHMDA.
DOI	10.1186/s12967-017-1304-7
Language	英语
Funding Project	National Natural Science Foundation of China[61572506] ; National Natural Science Foundation of China[61772531]
WOS Research Area	Research & Experimental Medicine
WOS Subject	Medicine, Research & Experimental
WOS ID	WOS:000413308900001
Publisher	BIOMED CENTRAL LTD
Citation statistics
Document Type	期刊论文
Identifier	http://ir.amss.ac.cn/handle/2S8OKBNM/26788
Collection	应用数学研究所
Corresponding Author	You, Zhu-Hong; Chen, Xing
Affiliation	1.Xijing Univ, Dept Informat Engn, Xian 710123, Shaanxi, Peoples R China 2.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou, Peoples R China 3.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China 4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
Recommended Citation GB/T 7714	Huang, Yu-An,You, Zhu-Hong,Chen, Xing,et al. Prediction of microbe-disease association from the integration of neighbor and graph with collaborative recommendation model[J]. JOURNAL OF TRANSLATIONAL MEDICINE,2017,15:11.
APA	Huang, Yu-An,You, Zhu-Hong,Chen, Xing,Huang, Zhi-An,Zhang, Shanwen,&Yan, Gui-Ying.(2017).Prediction of microbe-disease association from the integration of neighbor and graph with collaborative recommendation model.JOURNAL OF TRANSLATIONAL MEDICINE,15,11.
MLA	Huang, Yu-An,et al."Prediction of microbe-disease association from the integration of neighbor and graph with collaborative recommendation model".JOURNAL OF TRANSLATIONAL MEDICINE 15(2017):11.

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