MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction.

ABSTRACT: Recently, a growing number of biological research and scientific experiments have demonstrated that microRNA (miRNA) affects the development of human complex diseases. Discovering miRNA-disease associations plays an increasingly vital role in devising diagnostic and therapeutic tools for diseases. However, since uncovering associations via experimental methods is expensive and time-consuming, novel and effective computational methods for association prediction are in demand. In this study, we developed a computational model of Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction (MDHGI) to discover new miRNA-disease associations by integrating the predicted association probability obtained from matrix decomposition through sparse learning method,

SUBMITTER: Chen X 

PROVIDER: S-EPMC6126877 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature