基于图神经网络的miRNA-疾病关联预测研究综述
Prediction Review of miRNA-Disease Association Based on Graph Neural Network
DOI: 10.12677/PM.2023.137196, PDF,   
作者: 张语凡：山东科技大学，数学与系统科学学院，山东 青岛
关键词: miRNA；疾病；关联预测；图神经网络；miRNA； Disease； Associations Prediction； Graph Neural Network
摘要: miRNA与复杂疾病之间的关联有着重要意义，研究复杂疾病组织中的miRNA的非正常表达为人类攻克相关复杂疾病提供了一个可行的解决方案。将己有的复杂疾病与miRNA的关联数据进行数学抽象建模，并设计合理、高效的算法实现预测未知关联预测，已经成为复杂疾病与miRNA的关联研究的主要内容。本文对图神经网络在miRNA-疾病关联预测中所涉及的关键技术，包括基于图神经网络的表示学习算法研究和miRNA疾病关联预测框架的研究现状、存在的问题以及面临的挑战进行系统综述。
Abstract: The association between miRNAs and complex diseases is of great significance, and studying the non-normal expression of miRNAs in complex disease tissues provides a feasible solution for human to overcome the related complex diseases. Modeling the existing association data of complex diseases and miRNAs by mathematical abstraction and designing reasonable and efficient algo-rithms to achieve prediction of unknown association prediction have become the main content of association studies of complex diseases and miRNAs. This paper provides a systematic review of the key technologies involved in graph neural networks in miRNA-disease association prediction, including the current status, problems and challenges of research on graph neural network-based representation learning algorithm research and miRNA disease association prediction framework.
文章引用：张语凡. 基于图神经网络的miRNA-疾病关联预测研究综述[J]. 理论数学, 2023, 13(7): 1911-1924. https://doi.org/10.12677/PM.2023.137196

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