School of Software, Xinjiang University, Urumqi, China.
School of Computer Science and Engineering, Central South University, Changsha, China.
BMC Bioinformatics. 2022 May 4;23(1):160. doi: 10.1186/s12859-022-04694-y.
Circular RNAs (circRNAs) play essential roles in cancer development and therapy resistance. Many studies have shown that circRNA is closely related to human health. The expression of circRNAs also affects the sensitivity of cells to drugs, thereby significantly affecting the efficacy of drugs. However, traditional biological experiments are time-consuming and expensive to validate drug-related circRNAs. Therefore, it is an important and urgent task to develop an effective computational method for predicting unknown circRNA-drug associations.
In this work, we propose a computational framework (GATECDA) based on graph attention auto-encoder to predict circRNA-drug sensitivity associations. In GATECDA, we leverage multiple databases, containing the sequences of host genes of circRNAs, the structure of drugs, and circRNA-drug sensitivity associations. Based on the data, GATECDA employs Graph attention auto-encoder (GATE) to extract the low-dimensional representation of circRNA/drug, effectively retaining critical information in sparse high-dimensional features and realizing the effective fusion of nodes' neighborhood information. Experimental results indicate that GATECDA achieves an average AUC of 89.18% under 10-fold cross-validation. Case studies further show the excellent performance of GATECDA.
Many experimental results and case studies show that our proposed GATECDA method can effectively predict the circRNA-drug sensitivity associations.
环状 RNA（circRNAs）在癌症发展和治疗耐药性中发挥着重要作用。许多研究表明，circRNA 与人类健康密切相关。circRNA 的表达也会影响细胞对药物的敏感性，从而显著影响药物的疗效。然而，传统的生物学实验需要花费大量的时间和金钱来验证与药物相关的 circRNA。因此，开发一种有效的计算方法来预测未知的 circRNA-药物关联是一项重要且紧迫的任务。
在这项工作中，我们提出了一种基于图注意自动编码器的计算框架（GATECDA），用于预测 circRNA-药物敏感性关联。在 GATECDA 中，我们利用多个数据库，包含 circRNA 宿主基因的序列、药物的结构以及 circRNA-药物敏感性关联。基于这些数据，GATECDA 采用图注意自动编码器（GATE）来提取 circRNA/药物的低维表示，有效地保留了稀疏高维特征中的关键信息，并实现了节点邻域信息的有效融合。实验结果表明，GATECDA 在 10 倍交叉验证下的平均 AUC 达到 89.18%。案例研究进一步表明了 GATECDA 的优异性能。
许多实验结果和案例研究表明，我们提出的 GATECDA 方法可以有效地预测 circRNA-药物敏感性关联。
