PDDGCN: A Parasitic Disease –Drug Association Predictor Based on Multi-view Fusion Graph Convolutional Network

In this study, we reorganized a fundamental dataset of parasitic disease–drug associations using the latest databases, and proposed a prediction model called PDDGCN, based on a multi-view graph convolutional network. To begin with, we fused similarity networks with binary networks to establish multi-view heterogeneous networks. We utilized neighborhood information aggregation layers to refine node embeddings within each view of the multi-vi ew heterogeneous networks, leveraging inter- and intra-domain message passing to aggregate information from neighboring nodes. Subsequently, we integrated multiple embeddings from each view and fed them into the ultimate discriminator. The experimental results demonstrate that PDDGCN outperforms fiv e state-of-the-art methods and four compared machine learning algorithms. Additionally, case studies have substantiated the effectiveness of PDDGCN in identifying associations between parasitic diseases and drugs. In summary, the PDDGCN model has the potential to facilitate the discovery of potentia l treatments for parasitic diseases and advance our comprehension of the etiology in this field. The source code is available athttps://github.com/AhauBioinformatics/PDDGCN.Graphical Abstract
Source: Interdisciplinary Sciences, Computational Life Sciences - Category: Bioinformatics Source Type: research