Lu PengLi, Wu Jinkai, Zhang Wenqi
School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China.
School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China.
Anal Biochem. 2024 Nov;694:115628. doi: 10.1016/j.ab.2024.115628. Epub 2024 Jul 26.
In recent years, with the in-depth study of circRNA, scholars have begun to discover a synergistic relationship between circRNA and microorganisms. Traditional wet lab experiments in biology require expensive financial, material, and human resources to investigate the relationship between circRNA and diseases. Therefore, we propose a new predictive model for inferring the association between circRNA and diseases, called HAGACDA. Specifically, we first aggregate the unique features of circRNA and diseases themselves through singular value decomposition, Pearson similarity, and the biological information characteristics of circRNA and diseases. Utilizing the competitive relationships between miRNA and other microorganisms, we construct a circRNA-miRNA-disease multi-source heterogeneous network. Subsequently, we use a relational graph attention network to aggregate features based on the structural connections between different nodes. To address the inherent limitations in capturing high-order patterns in edge sets, we integrate a hypergraph attention network to extract features of circRNA and diseases. Finally, association prediction scores for node pairs are obtained through a multilayer perceptron. We conducted a comprehensive analysis of the model, including comparative experiments and case studies. Experimental results demonstrate that our model accurately predicts the association between circRNA and diseases.
近年来，随着对环状RNA（circRNA）研究的深入，学者们开始发现circRNA与微生物之间的协同关系。生物学中传统的湿实验室实验需要耗费昂贵的资金、材料和人力资源来研究circRNA与疾病之间的关系。因此，我们提出了一种新的预测模型，用于推断circRNA与疾病之间的关联，称为HAGACDA。具体而言，我们首先通过奇异值分解、皮尔逊相似度以及circRNA和疾病的生物学信息特征，汇总circRNA和疾病本身的独特特征。利用微小RNA（miRNA）与其他微生物之间的竞争关系，我们构建了一个circRNA-miRNA-疾病多源异构网络。随后，我们使用关系图注意力网络基于不同节点之间的结构连接来汇总特征。为了解决在边集中捕获高阶模式时的固有局限性，我们集成了超图注意力网络来提取circRNA和疾病的特征。最后，通过多层感知器获得节点对的关联预测分数。我们对该模型进行了全面分析，包括对比实验和案例研究。实验结果表明，我们的模型能够准确预测circRNA与疾病之间的关联。
