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LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination
BACKGROUND: Researchers discover lncRNAs can act as decoys or sponges to regulate the behavior of miRNAs. Identification of lncRNA-miRNA interactions helps to understand the functions of lncRNAs, especially their roles in complicated diseases. Computational methods can save time and reduce cost in i...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6923828/ https://www.ncbi.nlm.nih.gov/pubmed/31856716 http://dx.doi.org/10.1186/s12864-019-6284-y |
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author | Zhang, Wen Tang, Guifeng Zhou, Shuang Niu, Yanqing |
author_facet | Zhang, Wen Tang, Guifeng Zhou, Shuang Niu, Yanqing |
author_sort | Zhang, Wen |
collection | PubMed |
description | BACKGROUND: Researchers discover lncRNAs can act as decoys or sponges to regulate the behavior of miRNAs. Identification of lncRNA-miRNA interactions helps to understand the functions of lncRNAs, especially their roles in complicated diseases. Computational methods can save time and reduce cost in identifying lncRNA-miRNA interactions, but there have been only a few computational methods. RESULTS: In this paper, we propose a sequence-derived linear neighborhood propagation method (SLNPM) to predict lncRNA-miRNA interactions. First, we calculate the integrated lncRNA-lncRNA similarity and the integrated miRNA-miRNA similarity by combining known lncRNA-miRNA interactions, lncRNA sequences and miRNA sequences. We consider two similarity calculation strategies respectively, namely similarity-based information combination (SC) and interaction profile-based information combination (PC). Second, the integrated lncRNA similarity-based graph and the integrated miRNA similarity-based graph are respectively constructed, and the label propagation processes are implemented on two graphs to score lncRNA-miRNA pairs. Finally, the weighted averages of their outputs are adopted as final predictions. Therefore, we construct two editions of SLNPM: sequence-derived linear neighborhood propagation method based on similarity information combination (SLNPM-SC) and sequence-derived linear neighborhood propagation method based on interaction profile information combination (SLNPM-PC). The experimental results show that SLNPM-SC and SLNPM-PC predict lncRNA-miRNA interactions with higher accuracy compared with other state-of-the-art methods. The case studies demonstrate that SLNPM-SC and SLNPM-PC help to find novel lncRNA-miRNA interactions for given lncRNAs or miRNAs. CONCLUSION: The study reveals that known interactions bring the most important information for lncRNA-miRNA interaction prediction, and sequences of lncRNAs (miRNAs) also provide useful information. In conclusion, SLNPM-SC and SLNPM-PC are promising for lncRNA-miRNA interaction prediction. |
format | Online Article Text |
id | pubmed-6923828 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69238282019-12-30 LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination Zhang, Wen Tang, Guifeng Zhou, Shuang Niu, Yanqing BMC Genomics Research BACKGROUND: Researchers discover lncRNAs can act as decoys or sponges to regulate the behavior of miRNAs. Identification of lncRNA-miRNA interactions helps to understand the functions of lncRNAs, especially their roles in complicated diseases. Computational methods can save time and reduce cost in identifying lncRNA-miRNA interactions, but there have been only a few computational methods. RESULTS: In this paper, we propose a sequence-derived linear neighborhood propagation method (SLNPM) to predict lncRNA-miRNA interactions. First, we calculate the integrated lncRNA-lncRNA similarity and the integrated miRNA-miRNA similarity by combining known lncRNA-miRNA interactions, lncRNA sequences and miRNA sequences. We consider two similarity calculation strategies respectively, namely similarity-based information combination (SC) and interaction profile-based information combination (PC). Second, the integrated lncRNA similarity-based graph and the integrated miRNA similarity-based graph are respectively constructed, and the label propagation processes are implemented on two graphs to score lncRNA-miRNA pairs. Finally, the weighted averages of their outputs are adopted as final predictions. Therefore, we construct two editions of SLNPM: sequence-derived linear neighborhood propagation method based on similarity information combination (SLNPM-SC) and sequence-derived linear neighborhood propagation method based on interaction profile information combination (SLNPM-PC). The experimental results show that SLNPM-SC and SLNPM-PC predict lncRNA-miRNA interactions with higher accuracy compared with other state-of-the-art methods. The case studies demonstrate that SLNPM-SC and SLNPM-PC help to find novel lncRNA-miRNA interactions for given lncRNAs or miRNAs. CONCLUSION: The study reveals that known interactions bring the most important information for lncRNA-miRNA interaction prediction, and sequences of lncRNAs (miRNAs) also provide useful information. In conclusion, SLNPM-SC and SLNPM-PC are promising for lncRNA-miRNA interaction prediction. BioMed Central 2019-12-20 /pmc/articles/PMC6923828/ /pubmed/31856716 http://dx.doi.org/10.1186/s12864-019-6284-y Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. |
spellingShingle | Research Zhang, Wen Tang, Guifeng Zhou, Shuang Niu, Yanqing LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination |
title | LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination |
title_full | LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination |
title_fullStr | LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination |
title_full_unstemmed | LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination |
title_short | LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination |
title_sort | lncrna-mirna interaction prediction through sequence-derived linear neighborhood propagation method with information combination |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6923828/ https://www.ncbi.nlm.nih.gov/pubmed/31856716 http://dx.doi.org/10.1186/s12864-019-6284-y |
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