CSpace  > 应用数学研究所
A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases
Chen, Xing1; Huang, Yu-An2; You, Zhu-Hong3; Yan, Gui-Ying4; Wang, Xue-Song1
2017-03-01
Source PublicationBIOINFORMATICS
ISSN1367-4803
Volume33Issue:5Pages:733-739
AbstractMotivation: Accumulating clinical observations have indicated that microbes living in the human body are closely associated with a wide range of human noninfectious diseases, which provides promising insights into the complex disease mechanism understanding. Predicting microbe-disease associations could not only boost human disease diagnostic and prognostic, but also improve the new drug development. However, little efforts have been attempted to understand and predict human microbe-disease associations on a large scale until now. Results: In this work, we constructed a microbe-human disease association network and further developed a novel computational model of KATZ measure for Human Microbe-Disease Association prediction (KATZHMDA) based on the assumption that functionally similar microbes tend to have similar interaction and non-interaction patterns with noninfectious diseases, and vice versa. To our knowledge, KATZHMDA is the first tool for microbe-disease association prediction. The reliable prediction performance could be attributed to the use of KATZ measurement, and the introduction of Gaussian interaction profile kernel similarity for microbes and diseases. LOOCV and k-fold cross validation were implemented to evaluate the effectiveness of this novel computational model based on known microbe-disease associations obtained from HMDAD database. As a result, KATZHMDA achieved reliable performance with average AUCs of 0.8130 +/- 0.0054, 0.8301 +/- 0.0033 and 0.8382 in 2-fold and 5-fold cross validation and LOOCV framework, respectively. It is anticipated that KATZHMDA could be used to obtain more novel microbes associated with important noninfectious human diseases and therefore benefit drug discovery and human medical improvement.
DOI10.1093/bioinformatics/btw715
Language英语
Funding ProjectNational Natural Science Foundation of China[11301517] ; National Natural Science Foundation of China[11631014] ; National Natural Science Foundation of China[61572506] ; National Natural Science Foundation of China[11371355] ; Fundamental Research Funds for the Central Universities[2014YC07]
WOS Research AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS SubjectBiochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS IDWOS:000397265300014
PublisherOXFORD UNIV PRESS
Citation statistics
Cited Times:70[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/24949
Collection应用数学研究所
Affiliation1.China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou 221116, Peoples R China
2.Hong Kong Polytechn Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China
3.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Chen, Xing,Huang, Yu-An,You, Zhu-Hong,et al. A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases[J]. BIOINFORMATICS,2017,33(5):733-739.
APA Chen, Xing,Huang, Yu-An,You, Zhu-Hong,Yan, Gui-Ying,&Wang, Xue-Song.(2017).A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases.BIOINFORMATICS,33(5),733-739.
MLA Chen, Xing,et al."A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases".BIOINFORMATICS 33.5(2017):733-739.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Chen, Xing]'s Articles
[Huang, Yu-An]'s Articles
[You, Zhu-Hong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Chen, Xing]'s Articles
[Huang, Yu-An]'s Articles
[You, Zhu-Hong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Chen, Xing]'s Articles
[Huang, Yu-An]'s Articles
[You, Zhu-Hong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.