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

IF 3.9 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Xiaosong Wang, Guojun Chen, Hang Hu, Min Zhang, Yuan Rao, Zhenyu Yue
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引用次数: 0

Abstract

The precise identification of associations between diseases and drugs is paramount for comprehending the etiology and mechanisms underlying parasitic diseases. Computational approaches are highly effective in discovering and predicting disease-drug associations. However, the majority of these approaches primarily rely on link-based methodologies within distinct biomedical bipartite networks. 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-view 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 five 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 potential treatments for parasitic diseases and advance our comprehension of the etiology in this field. The source code is available at https://github.com/AhauBioinformatics/PDDGCN .

Abstract Image

PDDGCN:基于多视图融合图卷积网络的寄生虫病-药物关联预测器。
准确识别疾病与药物之间的关联对于理解寄生虫病的病因和发病机制至关重要。计算方法在发现和预测疾病与药物的关联方面非常有效。然而,这些方法大多主要依赖于不同生物医学二元网络中基于链接的方法。在这项研究中,我们利用最新的数据库重组了寄生虫病与药物关联的基本数据集,并提出了一种基于多视图卷积网络的预测模型,称为 PDDGCN。首先,我们将相似性网络与二元网络融合,建立了多视角异构网络。我们利用邻域信息聚合层来完善多视图异构网络每个视图中的节点嵌入,利用域间和域内消息传递来聚合邻近节点的信息。随后,我们整合了来自每个视图的多个嵌入,并将其输入到最终判别器中。实验结果表明,PDDGCN 优于五种最先进的方法和四种机器学习算法。此外,案例研究也证明了 PDDGCN 在识别寄生虫病与药物之间关联方面的有效性。总之,PDDGCN 模型有望促进寄生虫病潜在治疗方法的发现,并推动我们对该领域病因学的理解。源代码见 https://github.com/AhauBioinformatics/PDDGCN 。
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来源期刊
Interdisciplinary Sciences: Computational Life Sciences
Interdisciplinary Sciences: Computational Life Sciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
8.60
自引率
4.20%
发文量
55
期刊介绍: Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology. The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer. The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.
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