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LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe-Disease Association prediction
Wang, Fan1,2; Huang, Zhi-An3; Chen, Xing4; Zhu, Zexuan3; Wen, Zhenkun3; Zhao, Jiyun1; Yan, Gui-Ying5
2017-08-08
Source PublicationSCIENTIFIC REPORTS
ISSN2045-2322
Volume7Pages:11
AbstractAn increasing number of evidences indicate microbes are implicated in human physiological mechanisms, including complicated disease pathology. Some microbes have been demonstrated to be associated with diverse important human diseases or disorders. Through investigating these disease-related microbes, we can obtain a better understanding of human disease mechanisms for advancing medical scientific progress in terms of disease diagnosis, treatment, prevention, prognosis and drug discovery. Based on the known microbe-disease association network, we developed a semi-supervised computational model of (L) under bar aplacian (R) under bar egularized (L) under bar east (S) under bar quares for (H) under bar uman (M) under bar icrobe-(D) under bar isease (A) under bar ssociation (LRLSHMDA) by introducing Gaussian interaction profile kernel similarity calculation and Laplacian regularized least squares classifier. LRLSHMDA reached the reliable AUCs of 0.8909 and 0.7657 based on the global and local leave-one-out cross validations, respectively. In the framework of 5-fold cross validation, average AUC value of 0.8794 +/-0.0029 further demonstrated its promising prediction ability. In case studies, 9, 9 and 8 of top-10 predicted microbes have been manually certified to be associated with asthma, colorectal carcinoma and chronic obstructive pulmonary disease by published literature evidence. Our proposed model achieves better prediction performance relative to the previous model. We expect that LRLSHMDA could offer insights into identifying more promising human microbe-disease associations in the future.
DOI10.1038/s41598-017-08127-2
Language英语
Funding ProjectPriority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; annual general university graduate research and innovation program of Jiangsu Province, China[KYLX16_0526] ; National Natural Science Foundation of China[11631014] ; National Natural Science Foundation of China[61471246] ; National Natural Science Foundation of China[11371355] ; Guangdong Foundation of Outstanding Young Teachers in Higher Education Institutions[Yq2013141] ; Guangdong Special Support Program of Top-notch Young Professionals[2014TQ01 x 273]
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000407180400031
PublisherNATURE PUBLISHING GROUP
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/26373
Collection应用数学研究所
Corresponding AuthorChen, Xing; Zhu, Zexuan
Affiliation1.China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 221116, Peoples R China
2.China Univ Min & Technol, Jiangsu Key Lab Mine Mech & Elect Equipment, Xuzhou 221116, Peoples R China
3.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China
4.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Peoples R China
5.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Fan,Huang, Zhi-An,Chen, Xing,et al. LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe-Disease Association prediction[J]. SCIENTIFIC REPORTS,2017,7:11.
APA Wang, Fan.,Huang, Zhi-An.,Chen, Xing.,Zhu, Zexuan.,Wen, Zhenkun.,...&Yan, Gui-Ying.(2017).LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe-Disease Association prediction.SCIENTIFIC REPORTS,7,11.
MLA Wang, Fan,et al."LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe-Disease Association prediction".SCIENTIFIC REPORTS 7(2017):11.
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