Cargando…
Double matrix completion for circRNA-disease association prediction
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-consum...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8185931/ https://www.ncbi.nlm.nih.gov/pubmed/34103016 http://dx.doi.org/10.1186/s12859-021-04231-3 |
_version_ | 1783704858065371136 |
---|---|
author | Zuo, Zong-Lan Cao, Rui-Fen Wei, Pi-Jing Xia, Jun-Feng Zheng, Chun-Hou |
author_facet | Zuo, Zong-Lan Cao, Rui-Fen Wei, Pi-Jing Xia, Jun-Feng Zheng, Chun-Hou |
author_sort | Zuo, Zong-Lan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8185931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81859312021-06-09 Double matrix completion for circRNA-disease association prediction Zuo, Zong-Lan Cao, Rui-Fen Wei, Pi-Jing Xia, Jun-Feng Zheng, Chun-Hou BMC Bioinformatics Research 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. BioMed Central 2021-06-08 /pmc/articles/PMC8185931/ /pubmed/34103016 http://dx.doi.org/10.1186/s12859-021-04231-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zuo, Zong-Lan Cao, Rui-Fen Wei, Pi-Jing Xia, Jun-Feng Zheng, Chun-Hou Double matrix completion for circRNA-disease association prediction |
title | Double matrix completion for circRNA-disease association prediction |
title_full | Double matrix completion for circRNA-disease association prediction |
title_fullStr | Double matrix completion for circRNA-disease association prediction |
title_full_unstemmed | Double matrix completion for circRNA-disease association prediction |
title_short | Double matrix completion for circRNA-disease association prediction |
title_sort | double matrix completion for circrna-disease association prediction |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8185931/ https://www.ncbi.nlm.nih.gov/pubmed/34103016 http://dx.doi.org/10.1186/s12859-021-04231-3 |
work_keys_str_mv | AT zuozonglan doublematrixcompletionforcircrnadiseaseassociationprediction AT caoruifen doublematrixcompletionforcircrnadiseaseassociationprediction AT weipijing doublematrixcompletionforcircrnadiseaseassociationprediction AT xiajunfeng doublematrixcompletionforcircrnadiseaseassociationprediction AT zhengchunhou doublematrixcompletionforcircrnadiseaseassociationprediction |