College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, People's Republic of China.
Institute of Wound Prevention and Treatment, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China.
BMC Bioinformatics. 2024 Oct 24;25(1):336. doi: 10.1186/s12859-024-05959-4.
As noncoding RNAs, circular RNAs (circRNAs) can act as microRNA (miRNA) sponges due to their abundant miRNA binding sites, allowing them to regulate gene expression and influence disease development. Accurately identifying circRNA-miRNA associations (CMAs) is helpful to understand complex disease mechanisms. Given that biological experiments are time consuming and labor intensive, alternative computational methods to predict CMAs are urgently needed.
This study proposes a novel computational model named CMAGN, which incorporates several advanced computational methods, for predicting CMAs. First, similarity networks for circRNAs and miRNAs are constructed according to their sequences. Graph attention autoencoder is then applied to these networks to generate the first representations of circRNAs and miRNAs. The second representations of circRNAs and miRNAs are obtained from the CMA network via node2vec. The similarity networks of circRNAs and miRNAs are reconstructed on the basis of these new representations. Finally, network consistency projection is applied to the reconstructed similarity networks and the CMA network to generate a recommendation matrix.
Five-fold cross-validation of CMAGN reveals that the area under ROC and PR curves exceed 0.96 on two widely used CMA datasets, outperforming several existing models. Additional tests elaborate the reasonability of the architecture of CMAGN and uncover its strengths and weaknesses.
作为非编码 RNA，circRNA 由于其丰富的 miRNA 结合位点，可作为 miRNA 海绵，从而调控基因表达并影响疾病发生发展。准确识别 circRNA-miRNA 相互作用（CMA）有助于理解复杂的疾病机制。鉴于生物实验耗时耗力，因此迫切需要替代的计算方法来预测 CMA。
本研究提出了一种名为 CMAGN 的新型计算模型，该模型整合了几种先进的计算方法，用于预测 CMA。首先，根据 circRNA 和 miRNA 的序列构建相似性网络。然后，将图注意自动编码器应用于这些网络，以生成 circRNA 和 miRNA 的第一表示。通过 node2vec 从 CMA 网络中获取 circRNA 和 miRNA 的第二表示。基于这些新表示，重新构建 circRNA 和 miRNA 的相似性网络。最后，将网络一致性投影应用于重新构建的相似性网络和 CMA 网络，以生成推荐矩阵。
CMAGN 的五重交叉验证表明，在两个广泛使用的 CMA 数据集上，ROC 和 PR 曲线下面积均超过 0.96，优于几种现有模型。进一步的测试阐述了 CMAGN 架构的合理性，并揭示了其优缺点。
