HEDDI-Net: heterogeneous network embedding for drug-disease association prediction and drug repurposing, with application to Alzheimer's disease.

ABSTRACT:

Background

The traditional process of developing new drugs is time-consuming and often unsuccessful, making drug repurposing an appealing alternative due to its speed and safety. Graph neural networks (GCNs) have emerged as a leading approach for predicting drug-disease associations by integrating drug and disease-related networks with advanced deep learning algorithms. However, GCNs generally infer association probabilities only for existing drugs and diseases, requiring network re-establishment and retraining for novel entities. Additionally, these methods often struggle with sparse networks and fail to elucidate the biological mechanisms underlying newly predicted drugs.

Methods

To address the limitations of traditional methods, we developed HEDDI-Net, a heterogeneous emb

SUBMITTER: Su YY 

PROVIDER: S-EPMC11786366 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature