School of Computer Science and Engineering, Central South University,410075 Changsha, China.
Department of Electrical and Computer Engineering, University of California, San Diego,92093 CA, USA.
Brief Bioinform. 2023 Sep 22;24(6). doi: 10.1093/bib/bbad410.
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.
非编码 RNA（ncRNA）在许多人类疾病的发生和发展中起着关键作用。因此，近年来，研究 ncRNA 与疾病之间的关联引起了研究人员的极大关注。已经提出了各种计算方法来探索 ncRNA-疾病关系，其中图神经网络（GNN）是 ncRNA-疾病关联预测的最新方法。在本调查中，我们全面回顾了基于 GNN 的 ncRNA-疾病关联模型。首先，我们详细介绍了 ncRNA 和 GNN。接下来，我们深入探讨了采用 GNN 预测 ncRNA-疾病关联的动机，重点关注数据结构、图中的高阶连接和稀疏监督信号。随后，我们分析了在使用 GNN 预测 ncRNA-疾病关联时面临的挑战，涵盖图构建、特征传播和聚合以及模型优化。然后，我们详细总结和评估了现有的基于 GNN 的模型在 ncRNA-疾病关联中的性能。最后，我们探讨了这个快速发展领域的潜在未来研究方向。本调查为有兴趣利用 GNN 揭示 ncRNA 和疾病之间复杂关系的研究人员提供了有价值的资源。
