Li Guanghui, Yue Yingjie, Liang Cheng, Xiao Qiu, Ding Pingjian, Luo Jiawei
School of Information Engineering, East China Jiaotong University Nanchang 330013 China
School of Science, East China Jiaotong University Nanchang 330013 China
RSC Adv. 2019 Oct 16;9(57):33222-33228. doi: 10.1039/c9ra06133a. eCollection 2019 Oct 15.
A growing body of evidence indicates that circular RNAs (circRNAs) play a pivotal role in various biological processes and have a close association with the initiation and progression of diseases. Moreover, circRNAs are considered as promising biomarkers for disease diagnosis owing to their characteristics of conservation, stability and universality. Inferring disease-circRNA relationships will contribute to the understanding of disease pathology. However, it is costly and laborious to discover novel disease-circRNA interactions by wet-lab experiments, and few computational methods have been devoted to predicting potential circRNAs for diseases. Here, we advance a computational method (NCPCDA) to identify novel circRNA-disease associations based on network consistency projection. For starters, we make use of multi-view similarity data, including circRNA functional similarity, disease semantic similarity, and association profile similarity, to construct the integrated circRNA similarity and disease similarity. Then, we project circRNA space and disease space on the circRNA-disease interaction network, respectively. Finally, we can obtain the predicted circRNA-disease association score matrix by combining the above two space projection scores. Simulation results show that NCPCDA can efficiently infer disease-circRNA relationships with high accuracy, obtaining AUCs of 0.9541 and 0.9201 in leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, case studies also suggest that NCPCDA is promising for discovering new disease-circRNA interactions. The NCPCDA dataset and code, as well as the detailed readme file for our code, can be downloaded from Github (https://github.com/ghli16/NNCPCD).
越来越多的证据表明，环状RNA（circRNA）在各种生物学过程中发挥着关键作用，并且与疾病的发生和发展密切相关。此外，由于circRNA具有保守性、稳定性和普遍性等特点，它们被认为是疾病诊断中有前景的生物标志物。推断疾病与circRNA的关系将有助于理解疾病病理学。然而，通过湿实验室实验发现新的疾病与circRNA相互作用既昂贵又费力，并且很少有计算方法致力于预测疾病潜在的circRNA。在此，我们提出一种基于网络一致性投影的计算方法（NCPCDA）来识别新的circRNA与疾病的关联。首先，我们利用多视图相似性数据，包括circRNA功能相似性、疾病语义相似性和关联谱相似性，来构建整合的circRNA相似性和疾病相似性。然后，我们分别在circRNA与疾病的相互作用网络上投影circRNA空间和疾病空间。最后，通过结合上述两个空间投影分数，我们可以获得预测的circRNA与疾病的关联分数矩阵。模拟结果表明，NCPCDA能够高效且准确地推断疾病与circRNA的关系，在留一法交叉验证和五折交叉验证中分别获得了0.9541和0.9201的曲线下面积（AUC）。此外，案例研究也表明NCPCDA在发现新的疾病与circRNA相互作用方面很有前景。NCPCDA数据集和代码，以及我们代码的详细自述文件，可以从Github（https://github.com/ghli16/NNCPCD）下载。
