School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.
School of Computational Science and Engineering, University of South Carolina, Columbia, U.S.
Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbaa409.
Relationship of accurate associations between non-coding RNAs and diseases could be of great help in the treatment of human biomedical research. However, the traditional technology is only applied on one type of non-coding RNA or a specific disease, and the experimental method is time-consuming and expensive. More computational tools have been proposed to detect new associations based on known ncRNA and disease information. Due to the ncRNAs (circRNAs, miRNAs and lncRNAs) having a close relationship with the progression of various human diseases, it is critical for developing effective computational predictors for ncRNA-disease association prediction. In this paper, we propose a new computational method of three-matrix factorization with hypergraph regularization terms (HGRTMF) based on central kernel alignment (CKA), for identifying general ncRNA-disease associations. In the process of constructing the similarity matrix, various types of similarity matrices are applicable to circRNAs, miRNAs and lncRNAs. Our method achieves excellent performance on five datasets, involving three types of ncRNAs. In the test, we obtain best area under the curve scores of $0.9832$, $0.9775$, $0.9023$, $0.8809$ and $0.9185$ via 5-fold cross-validation and $0.9832$, $0.9836$, $0.9198$, $0.9459$ and $0.9275$ via leave-one-out cross-validation on five datasets. Furthermore, our novel method (CKA-HGRTMF) is also able to discover new associations between ncRNAs and diseases accurately. Availability: Codes and data are available: https://github.com/hzwh6910/ncRNA2Disease.git. Contact:fguo@tju.edu.cn.
非编码 RNA 与疾病之间准确关联的关系可能对人类生物医学研究的治疗有很大帮助。然而，传统技术仅应用于一种类型的非编码 RNA 或特定疾病，且实验方法耗时且昂贵。已经提出了更多的计算工具，以便基于已知的 ncRNA 和疾病信息来检测新的关联。由于 ncRNA（circRNAs、miRNAs 和 lncRNAs）与各种人类疾病的进展密切相关，因此开发有效的 ncRNA-疾病关联预测计算预测器至关重要。在本文中，我们提出了一种基于中心核对准（CKA）的三矩阵因式分解与超图正则化项（HGRTMF）的新计算方法，用于识别一般的 ncRNA-疾病关联。在构建相似性矩阵的过程中，各种类型的相似性矩阵都适用于 circRNAs、miRNAs 和 lncRNAs。我们的方法在涉及三种 ncRNA 的五个数据集上均取得了出色的性能。在测试中，我们通过 5 折交叉验证获得了最佳的曲线下面积分数为 0.9832、0.9775、0.9023、0.8809 和 0.9185，通过 5 个数据集的留一交叉验证获得了最佳的曲线下面积分数为 0.9832、0.9836、0.9198、0.9459 和 0.9275。此外，我们的新方法（CKA-HGRTMF）还能够准确地发现 ncRNA 和疾病之间的新关联。可获取性：代码和数据可在以下网址获取：https://github.com/hzwh6910/ncRNA2Disease.git。联系人：fguo@tju.edu.cn。
