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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...

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Autores principales: Zuo, Zong-Lan, Cao, Rui-Fen, Wei, Pi-Jing, Xia, Jun-Feng, Zheng, Chun-Hou
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
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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.
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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
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