MLNGCF: circRNA-disease associations prediction with multilayer attention neural graph-based collaborative filtering.

ABSTRACT:

Motivation

CircRNAs play a critical regulatory role in physiological processes, and the abnormal expression of circRNAs can mediate the processes of diseases. Therefore, exploring circRNAs-disease associations is gradually becoming an important area of research. Due to the high cost of validating circRNA-disease associations using traditional wet-lab experiments, novel computational methods based on machine learning are gaining more and more attention in this field. However, current computational methods suffer to insufficient consideration of latent features in circRNA-disease interactions.

Results

In this study, a multilayer attention neural graph-based collaborative filtering (MLNGCF) is proposed. MLNGCF first enhances multiple biological information with autoencoder as t

SUBMITTER: Wu Q 

PROVIDER: S-EPMC10457666 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature