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Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network
BACKGROUND: Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA–protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120853/ https://www.ncbi.nlm.nih.gov/pubmed/33985444 http://dx.doi.org/10.1186/s12859-021-04171-y |
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author | Li, Ying Sun, Hang Feng, Shiyao Zhang, Qi Han, Siyu Du, Wei |
author_facet | Li, Ying Sun, Hang Feng, Shiyao Zhang, Qi Han, Siyu Du, Wei |
author_sort | Li, Ying |
collection | PubMed |
description | BACKGROUND: Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA–protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs. RESULTS: We present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement. CONCLUSIONS: This study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver (http://csbg-jlu.site/lpc/predict) is developed to be convenient for users. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04171-y. |
format | Online Article Text |
id | pubmed-8120853 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81208532021-05-17 Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network Li, Ying Sun, Hang Feng, Shiyao Zhang, Qi Han, Siyu Du, Wei BMC Bioinformatics Methodology Article BACKGROUND: Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA–protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs. RESULTS: We present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement. CONCLUSIONS: This study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver (http://csbg-jlu.site/lpc/predict) is developed to be convenient for users. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04171-y. BioMed Central 2021-05-13 /pmc/articles/PMC8120853/ /pubmed/33985444 http://dx.doi.org/10.1186/s12859-021-04171-y 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 | Methodology Article Li, Ying Sun, Hang Feng, Shiyao Zhang, Qi Han, Siyu Du, Wei Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network |
title | Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network |
title_full | Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network |
title_fullStr | Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network |
title_full_unstemmed | Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network |
title_short | Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network |
title_sort | capsule-lpi: a lncrna–protein interaction predicting tool based on a capsule network |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120853/ https://www.ncbi.nlm.nih.gov/pubmed/33985444 http://dx.doi.org/10.1186/s12859-021-04171-y |
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