LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe–Disease Association prediction

Abstract An 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-relate...

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Autores principales:	Fan Wang, Zhi-An Huang, Xing Chen, Zexuan Zhu, Zhenkun Wen, Jiyun Zhao, Gui-Ying Yan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2e229a61ac344ff09129bf97747f5b88
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spelling	oai:doaj.org-article:2e229a61ac344ff09129bf97747f5b882021-12-02T15:06:17ZLRLSHMDA: Laplacian Regularized Least Squares for Human Microbe–Disease Association prediction10.1038/s41598-017-08127-22045-2322https://doaj.org/article/2e229a61ac344ff09129bf97747f5b882017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08127-2https://doaj.org/toc/2045-2322Abstract An 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 Laplacian Regularized Least Squares for Human Microbe–Disease Association (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.Fan WangZhi-An HuangXing ChenZexuan ZhuZhenkun WenJiyun ZhaoGui-Ying YanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fan Wang Zhi-An Huang Xing Chen Zexuan Zhu Zhenkun Wen Jiyun Zhao Gui-Ying Yan LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe–Disease Association prediction
description	Abstract An 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 Laplacian Regularized Least Squares for Human Microbe–Disease Association (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.
format	article
author	Fan Wang Zhi-An Huang Xing Chen Zexuan Zhu Zhenkun Wen Jiyun Zhao Gui-Ying Yan
author_facet	Fan Wang Zhi-An Huang Xing Chen Zexuan Zhu Zhenkun Wen Jiyun Zhao Gui-Ying Yan
author_sort	Fan Wang
title	LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe–Disease Association prediction
title_short	LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe–Disease Association prediction
title_full	LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe–Disease Association prediction
title_fullStr	LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe–Disease Association prediction
title_full_unstemmed	LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe–Disease Association prediction
title_sort	lrlshmda: laplacian regularized least squares for human microbe–disease association prediction
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/2e229a61ac344ff09129bf97747f5b88
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LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe–Disease Association prediction

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