MiRNA-Disease association prediction via non-negative matrix factorization based matrix completion

Abstract

A large number of biological studies have shown that microRNAs (miRNAs) are closely related to the occurrence and development of various human diseases. Nowadays, more and more research has explored the relationship between miRNAs and human diseases. However, existing known associations are often sparse, and it is not easy to predict the potential miRNA-disease associations accurately from large amounts of biological data. Hence, how to predict these associations effectively is an exploratory scientific topic. In this work, we propose a new matrix completion algorithm based on non-negative matrix factorization (NMFMC) to infer potential miRNA-disease associations. In NMFMC, we decompose the miRNA-disease association matrix into a known part and an unknown part. In such a manner, the experimentally validated associations can be well preserved, and the potential associations can be better recovered. In addition, both disease similarity and miRNA similarity are embedded into the proposed model to assist the association recovering process. As a result, the non-negative matrix factorization, matrix completion and graph regularization constraints are integrated into a unified framework to serve miRNA-disease association prediction. The validity of our method is confirmed by global and local leave-one-out-cross-validation and achieves AUCs of 0.9165 and 0.8512, respectively, which is an effective improvement over previous methods. Furthermore, we conduct case studies on three widespread human diseases, and NMFMC is also applicable. For Colon Neoplasms, Prostate Neoplasms, and Breast Neoplasms, 45, 44, and 50 of the top 50 predictions based on existing associations are confirmed by experimental reports.