College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Math Biosci Eng. 2023 Nov 14;20(12):20553-20575. doi: 10.3934/mbe.2023909.
Increasing amounts of experimental studies have shown that circular RNAs (circRNAs) play important regulatory roles in human diseases through interactions with related microRNAs (miRNAs). CircRNAs have become new potential disease biomarkers and therapeutic targets. Predicting circRNA-disease association (CDA) is of great significance for exploring the pathogenesis of complex diseases, which can improve the diagnosis level of diseases and promote the targeted therapy of diseases. However, determination of CDAs through traditional clinical trials is usually time-consuming and expensive. Computational methods are now alternative ways to predict CDAs. In this study, a new computational method, named PCDA-HNMP, was designed. For obtaining informative features of circRNAs and diseases, a heterogeneous network was first constructed, which defined circRNAs, mRNAs, miRNAs and diseases as nodes and associations between them as edges. Then, a deep analysis was conducted on the heterogeneous network by extracting meta-paths connecting to circRNAs (diseases), thereby mining hidden associations between various circRNAs (diseases). These associations constituted the meta-path-induced networks for circRNAs and diseases. The features of circRNAs and diseases were derived from the aforementioned networks via mashup. On the other hand, miRNA-disease associations (mDAs) were employed to improve the model's performance. miRNA features were yielded from the meta-path-induced networks on miRNAs and circRNAs, which were constructed from the meta-paths connecting miRNAs and circRNAs in the heterogeneous network. A concatenation operation was adopted to build the features of CDAs and mDAs. Such representations of CDAs and mDAs were fed into XGBoost to set up the model. The five-fold cross-validation yielded an area under the curve (AUC) of 0.9846, which was better than those of some existing state-of-the-art methods. The employment of mDAs can really enhance the model's performance and the importance analysis on meta-path-induced networks shown that networks produced by the meta-paths containing validated CDAs provided the most important contributions.
越来越多的实验研究表明，环状 RNA（circRNA）通过与相关 microRNA（miRNA）的相互作用，在人类疾病中发挥重要的调控作用。circRNA 已成为新的潜在疾病生物标志物和治疗靶点。预测 circRNA-疾病关联（CDA）对于探索复杂疾病的发病机制具有重要意义，它可以提高疾病的诊断水平，促进疾病的靶向治疗。然而，通过传统临床试验确定 CDA 通常既费时又昂贵。计算方法现在是预测 CDA 的替代方法。在这项研究中，设计了一种新的计算方法，称为 PCDA-HNMP。为了获得 circRNA 和疾病的信息特征，首先构建了一个异构网络，其中将 circRNA、mRNA、miRNA 和疾病定义为节点，它们之间的关联定义为边。然后，通过提取连接 circRNA（疾病）的元路径，对异构网络进行深入分析，从而挖掘各种 circRNA（疾病）之间隐藏的关联。这些关联构成了 circRNA 和疾病的元路径诱导网络。通过 mashup 从上述网络中提取 circRNA 和疾病的特征。另一方面，miRNA-疾病关联（mDA）被用来提高模型的性能。miRNA 特征从 miRNA 和 circRNA 的元路径诱导网络中获得，这些网络是从异构网络中连接 miRNA 和 circRNA 的元路径构建的。采用串联操作构建 CDAs 和 mDAs 的特征。将 CDA 和 mDA 的表示形式输入 XGBoost 以建立模型。五重交叉验证得到的曲线下面积（AUC）为 0.9846，优于一些现有的最先进方法。mDA 的使用确实可以提高模型的性能，元路径诱导网络的重要性分析表明，包含已验证 CDA 的元路径生成的网络提供了最重要的贡献。
