A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations.

ABSTRACT:

Background

Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNA-disease associations for disease prognosis, diagnosis and therapy. Dozens of machine learning and deep learning algorithms have been adopted to this problem, yet it is still challenging to learn efficient low-dimensional representations from high-dimensional features of lncRNAs and diseases to predict unknown lncRNA-disease associations accurately.

Results

We proposed an end-to-end model, VGAELDA, which integrates variational inference and graph autoencoders for lncRNA-disease associations prediction. VGAELDA contains two kinds of graph autoencoders. Variational graph autoencoders (VGAE) infer representations fr

SUBMITTER: Shi Z 

PROVIDER: S-EPMC7983260 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature