Abstract
BackgroundCircular 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.ResultsIn 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.ConclusionThe results show that our method works well for recommending the potential circRNAs for a disease for biological experiments.
Highlights
Circular RNAs are a class of single-stranded RNA molecules with a closed-loop structure
Identifying Circular RNA (circRNA) associated with the disease can provide a better understanding of the pathogenesis of the disease at the molecular level and help identify biomarkers of the disease and the design of drugs
We propose a novel method, DMCCDA, to predict potential circRNA-disease associations for biological experiments to promote its efficiency and reduce resource consumption
Summary
Circular RNAs (circRNAs) are a class of single-stranded RNA molecules with a closed-loop structure. Much effort has been made to combine available data with different methods to predict potential circRNA-disease associations These methods can be broadly divided into two categories; the first is the network-based method, and the second is the machine learning-based method. Wang et al [15] constructed a model named GCNCDA, which extracts features by using the graph convolutional neural network and predicts the potential circRNA-disease associations by forest penalizing attributes (Forest PA) classifier. Wang et al [16] used a deep generative adversarial network to draw features from multi-source fusion information They employed a logistic model tree classifier to infer the potential circRNA-disease association. Efficient association prediction models for miRNAdisease, lncRNA-disease, drug-disease, and lncRNA-miRNA are all very helpful in the design of our models and the results analysis [22,23,24,25,26,27,28]
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