Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information.

ABSTRACT: Predicting the potential microRNA (miRNA) candidates associated with a disease helps in exploring the mechanisms of disease development. Most recent approaches have utilized heterogeneous information about miRNAs and diseases, including miRNA similarities, disease similarities, and miRNA-disease associations. However, these methods do not utilize the projections of miRNAs and diseases in a low-dimensional space. Thus, it is necessary to develop a method that can utilize the effective information in the low-dimensional space to predict potential disease-related miRNA candidates. We proposed a method based on non-negative matrix factorization, named DMAPred, to predict potential miRNA-disease associations. DMAPred exploits the similarities and associations of diseases and miRNAs, and it inte

SUBMITTER: Xuan P 

PROVIDER: S-EPMC6770973 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature