Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks.

ABSTRACT: Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology and pathogenesis. Most previous methods focus on integrating similarities and associating information contained in heterogeneous miRNA-disease networks. However, these methods establish only shallow prediction models that fail to capture complex relationships among miRNA similarities, disease similarities, and miRNA-disease associations. We propose a prediction method on the basis of network representation learning and convolutional neural networks to predict disease miRNAs, called CNNMDA. CNNMDA deeply integrates the similarity information of miRNAs and diseases, miRNA-disease associations, and representations of miRNAs and diseases in low-dimensional feature space. The new framework based

SUBMITTER: Xuan P 

PROVIDER: S-EPMC6696449 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature