SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.

miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely associated with many human diseases. In this study, we proposed a computational model based on Similarity Constrained Matrix Factorizat...

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Autores principales:	Lei Li, Zhen Gao, Yu-Tian Wang, Ming-Wen Zhang, Jian-Cheng Ni, Chun-Hou Zheng, Yansen Su
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/5820cdebd2fa4b568026d5d287446885
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spelling	oai:doaj.org-article:5820cdebd2fa4b568026d5d2874468852021-12-02T19:57:33ZSCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.1553-734X1553-735810.1371/journal.pcbi.1009165https://doaj.org/article/5820cdebd2fa4b568026d5d2874468852021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009165https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely associated with many human diseases. In this study, we proposed a computational model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA). In order to effectively combine different disease and miRNA similarity data, we applied similarity network fusion algorithm to obtain integrated disease similarity (composed of disease functional similarity, disease semantic similarity and disease Gaussian interaction profile kernel similarity) and integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity and miRNA Gaussian interaction profile kernel similarity). In addition, the L2 regularization terms and similarity constraint terms were added to traditional Nonnegative Matrix Factorization algorithm to predict disease-related miRNAs. SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, the case studies on two common human diseases were also implemented to demonstrate the prediction accuracy of SCMFMDA. The out of top 50 predicted miRNAs confirmed by experimental reports that indicated SCMFMDA was effective for prediction of relationship between miRNAs and diseases.Lei LiZhen GaoYu-Tian WangMing-Wen ZhangJian-Cheng NiChun-Hou ZhengYansen SuPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1009165 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Lei Li Zhen Gao Yu-Tian Wang Ming-Wen Zhang Jian-Cheng Ni Chun-Hou Zheng Yansen Su SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.
description	miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely associated with many human diseases. In this study, we proposed a computational model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA). In order to effectively combine different disease and miRNA similarity data, we applied similarity network fusion algorithm to obtain integrated disease similarity (composed of disease functional similarity, disease semantic similarity and disease Gaussian interaction profile kernel similarity) and integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity and miRNA Gaussian interaction profile kernel similarity). In addition, the L2 regularization terms and similarity constraint terms were added to traditional Nonnegative Matrix Factorization algorithm to predict disease-related miRNAs. SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, the case studies on two common human diseases were also implemented to demonstrate the prediction accuracy of SCMFMDA. The out of top 50 predicted miRNAs confirmed by experimental reports that indicated SCMFMDA was effective for prediction of relationship between miRNAs and diseases.
format	article
author	Lei Li Zhen Gao Yu-Tian Wang Ming-Wen Zhang Jian-Cheng Ni Chun-Hou Zheng Yansen Su
author_facet	Lei Li Zhen Gao Yu-Tian Wang Ming-Wen Zhang Jian-Cheng Ni Chun-Hou Zheng Yansen Su
author_sort	Lei Li
title	SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.
title_short	SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.
title_full	SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.
title_fullStr	SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.
title_full_unstemmed	SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.
title_sort	scmfmda: predicting microrna-disease associations based on similarity constrained matrix factorization.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/5820cdebd2fa4b568026d5d287446885
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SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.

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