Abstract：
Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA-disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA-disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA-disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA-disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA-disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA-disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases.
摘要：
非编码RNA(ncRNAs)在许多人类疾病的发生和发展中起着至关重要的作用。因此,近年来，研究ncRNAs与疾病之间的关联引起了研究者的极大关注。已经提出了各种计算方法来探索ncRNA与疾病的关系，图神经网络（GNN）成为ncRNA-疾病关联预测的最新方法。在这次调查中,我们对基于GNN的ncRNA-疾病关联模型进行了全面综述.首先,我们提供了对ncRNAs和GNNs的详细介绍。接下来,我们深入研究了采用GNN预测ncRNA-疾病关联背后的动机，专注于数据结构，图和稀疏监督信号中的高阶连通性。随后，我们分析了使用GNN预测ncRNA-疾病关联的挑战，覆盖图构造，特征传播和聚合，和模型优化。然后，我们在ncRNA-疾病关联的背景下，对现有的基于GNN的模型进行了详细的总结和性能评估。最后,我们在这个快速发展的领域探索潜在的未来研究方向。这项调查对于有兴趣利用GNN来揭示ncRNAs与疾病之间的复杂关系的研究人员来说是一个宝贵的资源。
