Double matrix completion for circRNA-disease association prediction
Open Access
- 8 June 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in BMC Bioinformatics
- Vol. 22 (1), 1-15
- https://doi.org/10.1186/s12859-021-04231-3
Abstract
Background 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. Results 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. Conclusion The results show that our method works well for recommending the potential circRNAs for a disease for biological experiments.Keywords
This publication has 40 references indexed in Scilit:
- iCircDA-MF: identification of circRNA-disease associations based on matrix factorizationBriefings in Bioinformatics, 2019
- Circular RNAs and their roles in head and neck cancersMolecular Cancer, 2019
- DWNN-RLS: regularized least squares method for predicting circRNA-disease associationsBMC Bioinformatics, 2018
- PWCDA: Path Weighted Method for Predicting circRNA-Disease AssociationsInternational Journal of Molecular Sciences, 2018
- Circ2Disease: a manually curated database of experimentally validated circRNAs in human diseaseScientific Reports, 2018
- circRNA disease: a manually curated database of experimentally supported circRNA-disease associationsCell Death & Disease, 2018
- Prediction of CircRNA-Disease Associations Using KATZ Model Based on Heterogeneous NetworksInternational Journal of Biological Sciences, 2018
- CircR2Disease: a manually curated database for experimentally supported circular RNAs associated with various diseasesDatabase: The Journal of Biological Databases and Curation, 2018
- Understanding tissue-specificity with human tissue-specific regulatory networksScience China Information Sciences, 2016
- Circular RNA is enriched and stable in exosomes: a promising biomarker for cancer diagnosisCell Research, 2015