Noncoding RNA(ncRNA) is closely related to drug resistance. Identifying the association between ncRNA and drug resistance is of great significance for drug development. Methods based on biological experiments are often time-consuming and small-scale. Therefore, developing computational methods to distinguish the association between ncRNA and drug resistance is urgent. We develop a computational framework called GSLRDA to predict the association between ncRNA and drug resistance in this work. First, the known ncRNA–drug resistance associations are modeled as a bipartite graph of ncRNA and drug. Then, GSLRDA uses the light graph convolutional network (lightGCN) to learn the vector representation of ncRNA and drug from the ncRNA–drug bipartite graph. In addition, GSLRDA uses different data augmentation methods to generate different views for ncRNA and drug nodes and performs self-supervised learning, further improving the quality of learned ncRNA and drug vector representations through contrastive learning between nodes. Finally, GSLRDA uses the inner product to predict the association between ncRNA and drug resistance. To the best of our knowledge, GSLRDA is the first to apply self-supervised learning in association prediction tasks in the field of bioinformatics. The experimental results show that GSLRDA takes an AUC value of 0.9101, higher than the other eight state-of-the-art models. In addition, case studies including two drugs further illustrate the effectiveness of GSLRDA in predicting the association between ncRNA and drug resistance. The code and data sets of GSLRDA are available at https://github.com/JJZ-code/GSLRDA.

中文翻译：

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具有自我监督学习的图神经网络用于非编码 RNA-耐药性关联预测

非编码RNA（ncRNA）与耐药性密切相关。识别ncRNA与耐药性之间的关联对于药物开发具有重要意义。基于生物实验的方法通常耗时且规模小。因此，迫切需要开发计算方法来区分 ncRNA 与耐药性之间的关联。我们开发了一个称为 GSLRDA 的计算框架来预测这项工作中 ncRNA 与耐药性之间的关联。首先，已知的 ncRNA-耐药性关联被建模为 ncRNA 和药物的二分图。然后，GSLRDA 使用光图卷积网络（lightGCN）从 ncRNA-drug 二部图中学习 ncRNA 和药物的向量表示。此外，GSLRDA 使用不同的数据增强方法为 ncRNA 和药物节点生成不同的视图并进行自我监督学习，通过节点之间的对比学习进一步提高学习的 ncRNA 和药物向量表示的质量。最后，GSLRDA 使用内积来预测 ncRNA 与耐药性之间的关联。据我们所知，GSLRDA 是第一个将自监督学习应用于生物信息学领域的关联预测任务的人。实验结果表明，GSLRDA 的 AUC 值为 0.9101，高于其他 8 个最先进的模型。此外，包括两种药物的案例研究进一步说明了 GSLRDA 在预测 ncRNA 与耐药性之间的关联方面的有效性。GSLRDA 的代码和数据集可在 https 获得：