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. 2021 Jul 12;17(7):e1009165.
doi: 10.1371/journal.pcbi.1009165. eCollection 2021 Jul.

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

Lei Li  1 , Zhen Gao  1 , Yu-Tian Wang  1 , Ming-Wen Zhang  1 , Jian-Cheng Ni  1 , Chun-Hou Zheng  1   2 , Yansen Su  2
Affiliations

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

Lei Li et al. PLoS Comput Biol. .

Abstract

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flow chart of SCMFMDA.
Fig 2
Fig 2. The details of similarity constrained matrix factorization.
Fig 3
Fig 3
The influence of parameters on SCMFMDA: (A) the influence of γ; (B) the influence of σ and ε.
Fig 4
Fig 4. AUC of global LOOCV compared with those of MSCHLMDA, ICFMDA and SACMDA.
Fig 5
Fig 5. AUC of 5-CV compared with those of MSCHLMDA, ICFMDA and SACMDA.
See this image and copyright information in PMC

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Grants and funding

This work was supported by the National Natural Science Foundation of China through grants 61873001 (C.Z., Y.W.), U19A2064 (C.Z.), 61872220 (C.Z.) and 11701318 (Y.W.), the Natural Science Foundation of Shandong Province grant ZR2020KC022 (J.N., Y.W., Z.G., L.L) and the Open Project of Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, No. MMC202006 (Y.W., L.L). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.