Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, China.
Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Putian, Fujian, China.
BMC Bioinformatics. 2021 Jun 8;22(1):307. doi: 10.1186/s12859-021-04231-3.
Circular RNAs (circRNAs) are a class of single-stranded RNA molecules with a closed-loop structure. A growing body of research has shown that circRNAs are closely related to the development of diseases. Because biological experiments to verify circRNA-disease associations are time-consuming and wasteful of resources, it is necessary to propose a reliable computational method to predict the potential candidate circRNA-disease associations for biological experiments to make them more efficient.
In this paper, we propose a double matrix completion method (DMCCDA) for predicting potential circRNA-disease associations. First, we constructed a similarity matrix of circRNA and disease according to circRNA sequence information and semantic disease information. We also built a Gauss interaction profile similarity matrix for circRNA and disease based on experimentally verified circRNA-disease associations. Then, the corresponding circRNA sequence similarity and semantic similarity of disease are used to update the association matrix from the perspective of circRNA and disease, respectively, by matrix multiplication. Finally, from the perspective of circRNA and disease, matrix completion is used to update the matrix block, which is formed by splicing the association matrix obtained in the previous step with the corresponding Gaussian similarity matrix. Compared with other approaches, the model of DMCCDA has a relatively good result in leave-one-out cross-validation and five-fold cross-validation. Additionally, the results of the case studies illustrate the effectiveness of the DMCCDA model.
The results show that our method works well for recommending the potential circRNAs for a disease for biological experiments.
Circular RNAs（circRNAs）是一类具有闭合环结构的单链 RNA 分子。越来越多的研究表明，circRNAs 与疾病的发生发展密切相关。由于验证 circRNA-疾病关联的生物学实验既耗时又浪费资源，因此有必要提出一种可靠的计算方法来预测潜在的候选 circRNA-疾病关联，以便使这些实验更高效。
本文提出了一种用于预测潜在 circRNA-疾病关联的双矩阵补全方法（DMCCDA）。首先，我们根据 circRNA 序列信息和语义疾病信息构建了 circRNA 和疾病的相似性矩阵。我们还基于实验验证的 circRNA-疾病关联构建了 circRNA 和疾病的高斯互作用分布相似性矩阵。然后，通过矩阵乘法，分别从 circRNA 和疾病的角度利用相应的 circRNA 序列相似性和语义相似性来更新关联矩阵。最后，从 circRNA 和疾病的角度，通过矩阵补全来更新由上一步获得的关联矩阵与相应的高斯相似性矩阵拼接而成的矩阵块。与其他方法相比，DMCCDA 模型在留一交叉验证和五折交叉验证中具有相对较好的结果。此外，案例研究的结果说明了 DMCCDA 模型的有效性。
结果表明，我们的方法在为生物学实验推荐疾病的潜在 circRNAs 方面效果良好。
