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PBHMDA: Path-Based Human Microbe-Disease Association Prediction
Huang, Zhi-An1; Chen, Xing2; Zhu, Zexuan1; Liu, Hongsheng3,4; Yan, Gui-Ying5; You, Zhu-Hong6; Wen, Zhenkun1
AbstractWith the advance of sequencing technology and microbiology, the microorganismshave been found to be closely related to various important human diseases. The increasing identification of human microbe-disease associations offers important in sights into the underlying disease mechanism understanding from the perspective of humanmicrobes, which are greatly helpful for investigating pathogenesis, promoting early diagnosis and improving precision medicine. However, the current knowledge in this domain is still limited and far from complete. Here, we present the computational modelof Path-Based Human Microbe-Disease Association prediction (PBHMDA) based on the integration of known microbe-disease associations and the Gaussian interactionprofile kernel similarity for microbes and diseases. A special depth-first search algorithm was implemented to traverse all possible paths between microbes and diseases for inferring the most possible disease-related microbes. As a result, PBHMDA obtained a reliable prediction performance with AUCs (The area under ROC curve) of 0.9169 and 0.8767 in the frameworks of both global and local leave-one-out cross validations,respectively. Based on 5-fold cross validation, average AUCs of 0.9082 +/- 0.0061 further demonstrated the efficiency of the proposed model. For the case studies of liver cirrhosis, type 1 diabetes, and asthma, 9, 7, and 9 out of predicted microbes in the top 10 have been confirmed by previously published experimental literatures,respectively. We have publicly released the prioritized microbe-disease associations,which may help to select the most potential pairs for further guiding the experimental confirmation. In conclusion, PBHMDA may have potential to boost the discovery of novelmicrobe-disease associations and aid future research efforts toward microbe involvement in human disease mechanism. The code and data of PBHMDA is freely available at http://www.escience.cn/system/file?fileId=85214.
Keywordmicrobes diseases path-based measure computational prediction model association network
Funding ProjectNational Natural Science Foundation of China[11301517] ; National Natural Science Foundation of China[11631014] ; National Natural Science Foundation of China[61471246] ; National Natural Science Foundation of China[31570160] ; National Natural Science Foundation of China[11371355] ; National Natural Science Foundation of China[61572506] ; National Natural Science Foundation of China[61572328] ; Guangdong Foundation of Outstanding Young Teachers in Higher Education Institutions[Yq2013141] ; Guangdong Special Support Program of Top-notch Young Professionals[2014TQ01X273] ; Innovation Team Project from the Education Department of Liaoning Province[LT2015011] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences ; China-UK Visual Information Processing Lab
WOS Research AreaMicrobiology
WOS SubjectMicrobiology
WOS IDWOS:000394543000001
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Document Type期刊论文
Corresponding AuthorChen, Xing; Zhu, Zexuan
Affiliation1.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China
2.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou, Peoples R China
3.Liaoning Univ, Sch Life Sci, Shenyang, Peoples R China
4.Res Ctr Comp Simulating & Informat Proc Biomacrom, Shenyang, Peoples R China
5.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
6.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi, Peoples R China
Recommended Citation
GB/T 7714
Huang, Zhi-An,Chen, Xing,Zhu, Zexuan,et al. PBHMDA: Path-Based Human Microbe-Disease Association Prediction[J]. FRONTIERS IN MICROBIOLOGY,2017,8:10.
APA Huang, Zhi-An.,Chen, Xing.,Zhu, Zexuan.,Liu, Hongsheng.,Yan, Gui-Ying.,...&Wen, Zhenkun.(2017).PBHMDA: Path-Based Human Microbe-Disease Association Prediction.FRONTIERS IN MICROBIOLOGY,8,10.
MLA Huang, Zhi-An,et al."PBHMDA: Path-Based Human Microbe-Disease Association Prediction".FRONTIERS IN MICROBIOLOGY 8(2017):10.
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