School of Software, Xinjiang University, Urumqi 830091, China.
School of Computer Science and Engineering, Central South University, Changsha 410083, China.
J Chem Inf Model. 2022 Aug 8;62(15):3676-3684. doi: 10.1021/acs.jcim.2c00367. Epub 2022 Jul 15.
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.
非编码 RNA(ncRNA) 与药物耐药性密切相关。鉴定 ncRNA 与药物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较小。因此，开发用于区分 ncRNA 与药物耐药性之间关联的计算方法迫在眉睫。在这项工作中，我们开发了一种称为 GSLRDA 的计算框架来预测 ncRNA 与药物耐药性之间的关联。首先，将已知的 ncRNA-药物耐药性关联建模为 ncRNA 和药物的二分图。然后，GSLRDA 使用轻图卷积网络 (lightGCN) 从 ncRNA-药物二分图中学习 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，并进行自监督学习，通过节点之间的对比学习进一步提高学习到的 ncRNA 和药物向量表示的质量。最后，GSLRDA 使用内积来预测 ncRNA 和药物耐药性之间的关联。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关联预测任务的方法。实验结果表明，GSLRDA 的 AUC 值为 0.9101，高于其他八个最先进的模型。此外，包括两种药物的案例研究进一步说明了 GSLRDA 在预测 ncRNA 与药物耐药性之间关联方面的有效性。GSLRDA 的代码和数据集可在 https://github.com/JJZ-code/GSLRDA 上获得。
