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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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-07-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009165&type=printable |
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| 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 |
| collection | DOAJ |
| 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 |
| id | doaj-art-5820cdebd2fa4b568026d5d287446885 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2021-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-5820cdebd2fa4b568026d5d2874468852025-08-20T02:01:04ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-07-01177e100916510.1371/journal.pcbi.1009165SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.Lei LiZhen GaoYu-Tian WangMing-Wen ZhangJian-Cheng NiChun-Hou ZhengYansen SumiRNAs 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.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009165&type=printable |
| spellingShingle | 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. PLoS Computational Biology |
| title | 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_short | SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization. |
| title_sort | scmfmda predicting microrna disease associations based on similarity constrained matrix factorization |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009165&type=printable |
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