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

• Published in: PLoS computational biology Vol. 17; no. 7; p. e1009165
• Main Authors: Li, Lei, Gao, Zhen, Wang, Yu-Tian, Zhang, Ming-Wen, Ni, Jian-Cheng, Zheng, Chun-Hou, Su, Yansen
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 12.07.2021; Public Library of Science (PLoS)
• Subjects: Algorithms; Biological activity; Biology and life sciences; Case studies; Computer and Information Sciences; Computer applications; Constraints; Disease; Diseases; Factorization; Gene expression; Health aspects; Kernels; Machine learning; Medicine and Health Sciences; MicroRNA; MicroRNAs; miRNA; Neighborhoods; Neural networks; Non-coding RNA; Performance evaluation; Physical Sciences; Predictions; Regularization; Research and Analysis Methods; Ribonucleic acid; RNA; RNA sequencing; Semantics; Similarity; Social Sciences; Technology application; Tumors; China
• DOI: 10.1371/journal.pcbi.1009165
• ISSN: 1553-7358; 1553-734X

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 L 2 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.