Hunan Normal University and Hunan Xiangjiang Artificial Intelligence Academy, China.
Hunan Normal University, China.
Brief Bioinform. 2021 Nov 5;22(6). doi: 10.1093/bib/bbab177.
Many studies have evidenced that circular RNAs (circRNAs) are important regulators in various pathological processes and play vital roles in many human diseases, which could serve as promising biomarkers for disease diagnosis, treatment and prognosis. However, the functions of most of circRNAs remain to be unraveled, and it is time-consuming and costly to uncover those relationships between circRNAs and diseases by conventional experimental methods. Thus, identifying candidate circRNAs for human diseases offers new opportunities to understand the functional properties of circRNAs and the pathogenesis of diseases. In this study, we propose a novel network embedding-based adaptive subspace learning method (NSL2CD) for predicting potential circRNA-disease associations and discovering those disease-related circRNA candidates. The proposed method first calculates disease similarities and circRNA similarities by fully utilizing different data sources and learns low-dimensional node representations with network embedding methods. Then, we adopt an adaptive subspace learning model to discover potential associations between circRNAs and diseases. Meanwhile, an integrated weighted graph regularization term is imposed to preserve local geometric structures of data spaces, and L1,2-norm constraint is also incorporated into the model to realize the smoothness and sparsity of projection matrices. The experiment results show that NSL2CD achieves comparable performance under different evaluation metrics, and case studies further confirm its ability to discover potential candidate circRNAs for human diseases.
许多研究表明，环状 RNA（circRNAs）是各种病理过程中的重要调节剂，在许多人类疾病中发挥着重要作用，它们可以作为疾病诊断、治疗和预后的有前途的生物标志物。然而，大多数 circRNAs 的功能仍有待揭示，并且通过传统的实验方法揭示 circRNAs 与疾病之间的关系既耗时又昂贵。因此，鉴定人类疾病的候选 circRNAs 为理解 circRNAs 的功能特性和疾病的发病机制提供了新的机会。在本研究中，我们提出了一种新的基于网络嵌入的自适应子空间学习方法（NSL2CD），用于预测潜在的 circRNA-疾病关联，并发现那些与疾病相关的 circRNA 候选物。该方法首先通过充分利用不同的数据源来计算疾病相似性和 circRNA 相似性，并利用网络嵌入方法学习低维节点表示。然后，我们采用自适应子空间学习模型来发现 circRNA 和疾病之间的潜在关联。同时，引入了一个集成的加权图正则化项来保留数据空间的局部几何结构，并且模型中还包含 L1,2-范数约束，以实现投影矩阵的平滑性和稀疏性。实验结果表明，NSL2CD 在不同的评价指标下表现相当，案例研究进一步证实了它发现人类疾病潜在候选 circRNA 的能力。
