MicroRNAs (miRNAs) are found ubiquitously in biological cells and play a pivotal role in regulating the expression of numerous target genes. Therapies centered around miRNAs are emerging as a promising strategy for disease treatment, aiming to intervene in disease progression by modulating abnormal miRNA expressions. The accurate prediction of miRNA–drug resistance (MDR) is crucial for the success of miRNA therapies. Computational models based on deep learning have demonstrated exceptional performance in predicting potential MDRs. However, their effectiveness can be compromised by errors in the data acquisition process, leading to inaccurate node representations. To address this challenge, we introduce the GAM-MDR model, which combines the graph autoencoder (GAE) with random path masking techniques to precisely predict potential MDRs. The reliability and effectiveness of the GAM-MDR model are mainly reflected in two aspects. Firstly, it efficiently extracts the representations of miRNA and drug nodes in the miRNA–drug network. Secondly, our designed random path masking strategy efficiently reconstructs critical paths in the network, thereby reducing the adverse impact of noisy data. To our knowledge, this is the first time that a random path masking strategy has been integrated into a GAE to infer MDRs. Our method was subjected to multiple validations on public datasets and yielded promising results. We are optimistic that our model could offer valuable insights for miRNA therapeutic strategies and deepen the understanding of the regulatory mechanisms of miRNAs. Our data and code are publicly available at GitHub:https://github.com/ZZCrazy00/GAM-MDR.

中文翻译：

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GAM-MDR：使用基于随机路径掩蔽的图形自动编码器探测 miRNA 耐药性

MicroRNA (miRNA) 普遍存在于生物细胞中，在调节众多靶基因的表达中发挥着关键作用。以 miRNA 为中心的疗法正在成为一种有前景的疾病治疗策略，旨在通过调节异常 miRNA 表达来干预疾病进展。准确预测 miRNA 耐药性 (MDR) 对于 miRNA 治疗的成功至关重要。基于深度学习的计算模型在预测潜在 MDR 方面表现出了卓越的性能。然而，它们的有效性可能会因数据采集过程中的错误而受到影响，从而导致节点表示不准确。为了应对这一挑战，我们引入了 GAM-MDR 模型，该模型将图自动编码器 (GAE) 与随机路径屏蔽技术相结合，以精确预测潜在的 MDR。GAM-MDR模型的可靠性和有效性主要体现在两个方面。首先，它有效地提取了 miRNA-药物网络中 miRNA 和药物节点的表示。其次，我们设计的随机路径屏蔽策略有效地重建了网络中的关键路径，从而减少了噪声数据的不利影响。据我们所知，这是第一次将随机路径屏蔽策略集成到 GAE 中来推断 MDR。我们的方法在公共数据集上进行了多次验证，并产生了有希望的结果。我们乐观地认为，我们的模型可以为 miRNA 治疗策略提供有价值的见解，并加深对 miRNA 调控机制的理解。我们的数据和代码可在 GitHub 上公开获取：https://github.com/ZZCrazy00/GAM-MDR。