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

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...