研究 miRNA 与疾病之间的复杂关系对于预防和治疗 miRNA 相关疾病至关重要。现有的计算方法往往忽视了不同节点特征的重要性以及特征在异构节点之间的传播。许多预测模型只关注miRNA和疾病的特征编码，而忽略了特征聚合的重要性。我们提出了一种通过双邻域特征聚合和特征融合的预测方法，该方法使用多个信息源，聚合同质和异构节点上的信息并融合学习的特征来预测疾病节点的多种表示。我们分别基于不同的相似度计算方法构建了多个同构节点的相似度网络，并利用图卷积网络融合注意力机制来获取不同重要程度的信息。为了缓解数据集中的稀疏连接问题，我们构建了一个两邻域异构图神经网络模型，利用已知的 miRNA-疾病关联信息将同质相似性网络集成到 miRNA-疾病异构网络中。我们使用与网络中节点关联的邻域信息来执行特征聚合。此外，我们使用特征融合模块来了解不同类型节点对预测 miRNA 与疾病关联的重要性。我们在人类 microRNA 疾病数据库 (HMDD v3.2) 上的实验结果表明，该模型表现出优越的性能。这项工作证明了我们的模型能够通过两种常见癌症的案例研究来识别与疾病相关的潜在 miRNA。版权所有 © 2024。由 Elsevier Ltd 出版。

Studying the intricate relationship between miRNAs and diseases is crucial to prevent and treat miRNA-related disorders. Existing computational methods often overlook the importance of features of different nodes and the propagation of features among heterogeneous nodes. Many prediction models focus only on the feature coding of miRNA and diseases and ignore the importance of feature aggregation. We propose a prediction method via dual-neighbourhood feature aggregation and feature fusion, which uses multiple sources of information, aggregates information on homogeneous and heterogeneous nodes and fuses learned features to predict multiple representations of disease nodes. We constructed similarity networks of multiple homogeneous nodes based on different similarity computation methods respectively, and fused the attention mechanism by using graph convolutional networks to obtain information of different levels of importance. To alleviate the problem of sparse connectivity in the dataset, we built a two-neighbourhood heterogeneous graph neural network model to integrate the homogeneous similarity network into a miRNA-disease heterogeneous network by using known miRNA-disease association information. We used the neighbourhood information associated with the nodes in the network to perform feature aggregation. In addition, we used a feature fusion module to learn the importance of different types of nodes to predict miRNA-disease associations. Our experimental results on the Human microRNA Disease Database (HMDD v3.2) show that the model demonstrates superior performance. This work demonstrates the capability of our model to identify potential miRNAs associated with diseases through a case study of two common cancers.Copyright © 2024. Published by Elsevier Ltd.