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RWRMTN: a tool for predicting disease-associated microRNAs based on a microRNA-target gene network
BACKGROUND: The misregulation of microRNA (miRNA) has been shown to cause diseases. Recently, we have proposed a computational method based on a random walk framework on a miRNA-target gene network to predict disease-associated miRNAs. The prediction performance of our method is better than that of...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7296691/ https://www.ncbi.nlm.nih.gov/pubmed/32539680 http://dx.doi.org/10.1186/s12859-020-03578-3 |
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author | Le, Duc-Hau Tran, Trang T. H. |
author_facet | Le, Duc-Hau Tran, Trang T. H. |
author_sort | Le, Duc-Hau |
collection | PubMed |
description | BACKGROUND: The misregulation of microRNA (miRNA) has been shown to cause diseases. Recently, we have proposed a computational method based on a random walk framework on a miRNA-target gene network to predict disease-associated miRNAs. The prediction performance of our method is better than that of some existing state-of-the-art network- and machine learning-based methods since it exploits the mutual regulation between miRNAs and their target genes in the miRNA-target gene interaction networks. RESULTS: To facilitate the use of this method, we have developed a Cytoscape app, named RWRMTN, to predict disease-associated miRNAs. RWRMTN can work on any miRNA-target gene network. Highly ranked miRNAs are supported with evidence from the literature. They then can also be visualized based on the rankings and in relationships with the query disease and their target genes. In addition, automation functions are also integrated, which allow RWRMTN to be used in workflows from external environments. We demonstrate the ability of RWRMTN in predicting breast and lung cancer-associated miRNAs via workflows in Cytoscape and other environments. CONCLUSIONS: Considering a few computational methods have been developed as software tools for convenient uses, RWRMTN is among the first GUI-based tools for the prediction of disease-associated miRNAs which can be used in workflows in different environments. |
format | Online Article Text |
id | pubmed-7296691 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-72966912020-06-16 RWRMTN: a tool for predicting disease-associated microRNAs based on a microRNA-target gene network Le, Duc-Hau Tran, Trang T. H. BMC Bioinformatics Software BACKGROUND: The misregulation of microRNA (miRNA) has been shown to cause diseases. Recently, we have proposed a computational method based on a random walk framework on a miRNA-target gene network to predict disease-associated miRNAs. The prediction performance of our method is better than that of some existing state-of-the-art network- and machine learning-based methods since it exploits the mutual regulation between miRNAs and their target genes in the miRNA-target gene interaction networks. RESULTS: To facilitate the use of this method, we have developed a Cytoscape app, named RWRMTN, to predict disease-associated miRNAs. RWRMTN can work on any miRNA-target gene network. Highly ranked miRNAs are supported with evidence from the literature. They then can also be visualized based on the rankings and in relationships with the query disease and their target genes. In addition, automation functions are also integrated, which allow RWRMTN to be used in workflows from external environments. We demonstrate the ability of RWRMTN in predicting breast and lung cancer-associated miRNAs via workflows in Cytoscape and other environments. CONCLUSIONS: Considering a few computational methods have been developed as software tools for convenient uses, RWRMTN is among the first GUI-based tools for the prediction of disease-associated miRNAs which can be used in workflows in different environments. BioMed Central 2020-06-15 /pmc/articles/PMC7296691/ /pubmed/32539680 http://dx.doi.org/10.1186/s12859-020-03578-3 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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 | Software Le, Duc-Hau Tran, Trang T. H. RWRMTN: a tool for predicting disease-associated microRNAs based on a microRNA-target gene network |
title | RWRMTN: a tool for predicting disease-associated microRNAs based on a microRNA-target gene network |
title_full | RWRMTN: a tool for predicting disease-associated microRNAs based on a microRNA-target gene network |
title_fullStr | RWRMTN: a tool for predicting disease-associated microRNAs based on a microRNA-target gene network |
title_full_unstemmed | RWRMTN: a tool for predicting disease-associated microRNAs based on a microRNA-target gene network |
title_short | RWRMTN: a tool for predicting disease-associated microRNAs based on a microRNA-target gene network |
title_sort | rwrmtn: a tool for predicting disease-associated micrornas based on a microrna-target gene network |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7296691/ https://www.ncbi.nlm.nih.gov/pubmed/32539680 http://dx.doi.org/10.1186/s12859-020-03578-3 |
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