2022年7月12日 · In our study, based on the known association information between metabolites and diseases, Gaussian kernel similarity of metabolites and integrated similarity combining disease semantic similarity and Gaussian kernel similarity, we establish a deep learning algorithmic model named GCNAT to predict potential associations between metabolites and ...

2023年10月31日 · 提出一种新的深度学习算法，名为图卷积网络与图注意力网络（gcnat），用于预测疾病相关代谢物的潜在关联。 首先，根据已知代谢物与疾病的关联，代谢物与代谢物的相似性以及疾病的相似性构建一个异构网络。

2022年7月18日 · In this work, we present a new deep learning algorithm named as graph convolutional network with graph attention network (GCNAT) to predict the potential associations of disease-related metabolites. First, we construct a heterogeneous network based on known metabolite-disease associations, metabolite-metabolite similarities and disease-disease ...

#GCNAT: A Graph-Based Deep Learning Algorithm for Predicting Disease-Metabolite Associations

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2024年12月27日 · In practical applications, we aim for the predictive model to provide precise host associations for novel viruses. Therefore, we analyzed the Top-K prediction accuracy of GCNAT, CHERRY, CL4PHI, and our model.

2024年8月7日 · In light of these limitations, we proposed a novel deep learning model based on metapath aggregation of tripartite heterogeneous networks (MAHN) to explore disease-related metabolites. Specifically, we introduced microbes to construct a tripartite heterogeneous network and employed graph convolutional network and enhanced GraphSAGE to learn ...

2023年7月18日 · Traditional biological experiments often rely on a lot of manpower to do repeated experiments, which is time consuming and labor intensive. To address this issue, we develop a deep learning model based on the auto-encoder and non-negative matrix factorization named as MDA-AENMF to predict the potential associations between metabolites and diseases.

GCNATMDA is a computational model designed to predict potential associations between microbes and drugs. It leverages the power of graph convolutional networks and graph attention networks to analyze complex interaction networks, based on datasets such as MDAD, Drugvirus, and aBiofilm. These ...

2019年4月21日 · 解析ICLR2018论文《Graph Attention Networks》，介绍图注意力网络 (GAT)在图神经网络 (GNN)中的重要性，以及如何通过注意力机制改进图卷积网络 (GCN)，在多个数据集上实现顶尖性能。 背景： ICLR2018 论文，Graph Attention Network在GNN中非常重要，再之前图卷积网络GCN的基础之上引入了 注意力机制，非常实用。 论文地址： https://arxiv.org/abs/1710.10903. 代码地址： https://github.com/Diego999/pyGAT. 相关论文详 …