IEEE/ACM Trans Comput Biol Bioinform. 2024 Sep-Oct;21(5):1445-1457. doi: 10.1109/TCBB.2024.3402248. Epub 2024 Oct 9.
As a series of single-stranded RNAs, circRNAs have been implicated in numerous diseases and can serve as valuable biomarkers for disease therapy and prevention. However, traditional biological experiments demand significant time and effort. Therefore, various computational methods have been proposed to address this limitation, but how to extract features more comprehensively remains a challenge that needs further attention in the future. In this study, we propose a unique approach to predict circRNA-disease associations based on resistance distance and graph attention network (RDGAN). First, the associations of circRNA and disease are obtained by fusing multiple databases, and resistance distance as a similarity matrix is used to further deal with the sparse of the similarity matrices. Then the circRNA-disease heterogeneous network is constructed based on the similiarity of circRNA-circRNA, disease-disease and the known circRNA-disease adjacency matric. Second, leveraging the three neural network modules-ResGatedGraphConv, GAT and MFConv-we gather node feature embeddings collected from the heterogeneous network. Subsequently, all the characteristics are supplied to the self-attention mechanism to predict new potential connections. Finally, our model obtains a remarkable AUC value of 0.9630 through five-fold cross-validation, surpassing the predictive performance of the other eight state-of-the-art models.
作为一系列单链 RNA，circRNAs 与许多疾病有关，可作为疾病治疗和预防的有价值的生物标志物。然而，传统的生物学实验需要大量的时间和精力。因此，已经提出了各种计算方法来解决这个限制，但如何更全面地提取特征仍然是未来需要进一步关注的挑战。在这项研究中，我们提出了一种基于阻力距离和图注意网络（RDGAN）的预测 circRNA-疾病关联的独特方法。首先，通过融合多个数据库获得 circRNA 和疾病的关联，并用阻力距离作为相似性矩阵进一步处理相似性矩阵的稀疏性。然后基于 circRNA-circRNA、疾病-疾病和已知 circRNA-疾病邻接矩阵的相似性构建 circRNA-疾病异质网络。其次，利用 ResGatedGraphConv、GAT 和 MFConv 三个神经网络模块，从异质网络中收集节点特征嵌入。随后，将所有特征提供给自注意机制以预测新的潜在连接。最后，我们的模型通过五重交叉验证获得了 0.9630 的出色 AUC 值，超过了其他八个最先进模型的预测性能。
