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Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks

BACKGROUND: Network alignment is an efficient computational framework in the prediction of protein function and phylogenetic relationships in systems biology. However, most of existing alignment methods focus on aligning PPIs based on static network model, which are actually dynamic in real-world sy...

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Autores principales: Zhong, Yuanke, Li, Jing, He, Junhao, Gao, Yiqun, Liu, Jie, Wang, Jingru, Shang, Xuequn, Hu, Jialu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7495832/
https://www.ncbi.nlm.nih.gov/pubmed/32938373
http://dx.doi.org/10.1186/s12859-020-03672-6
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author Zhong, Yuanke
Li, Jing
He, Junhao
Gao, Yiqun
Liu, Jie
Wang, Jingru
Shang, Xuequn
Hu, Jialu
author_facet Zhong, Yuanke
Li, Jing
He, Junhao
Gao, Yiqun
Liu, Jie
Wang, Jingru
Shang, Xuequn
Hu, Jialu
author_sort Zhong, Yuanke
collection PubMed
description BACKGROUND: Network alignment is an efficient computational framework in the prediction of protein function and phylogenetic relationships in systems biology. However, most of existing alignment methods focus on aligning PPIs based on static network model, which are actually dynamic in real-world systems. The dynamic characteristic of PPI networks is essential for understanding the evolution and regulation mechanism at the molecular level and there is still much room to improve the alignment quality in dynamic networks. RESULTS: In this paper, we proposed a novel alignment algorithm, Twadn, to align dynamic PPI networks based on a strategy of time warping. We compare Twadn with the existing dynamic network alignment algorithm DynaMAGNA++ and DynaWAVE and use area under the receiver operating characteristic curve and area under the precision-recall curve as evaluation indicators. The experimental results show that Twadn is superior to DynaMAGNA++ and DynaWAVE. In addition, we use protein interaction network of Drosophila to compare Twadn and the static network alignment algorithm NetCoffee2 and experimental results show that Twadn is able to capture timing information compared to NetCoffee2. CONCLUSIONS: Twadn is a versatile and efficient alignment tool that can be applied to dynamic network. Hopefully, its application can benefit the research community in the fields of molecular function and evolution.
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spelling pubmed-74958322020-09-23 Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks Zhong, Yuanke Li, Jing He, Junhao Gao, Yiqun Liu, Jie Wang, Jingru Shang, Xuequn Hu, Jialu BMC Bioinformatics Research BACKGROUND: Network alignment is an efficient computational framework in the prediction of protein function and phylogenetic relationships in systems biology. However, most of existing alignment methods focus on aligning PPIs based on static network model, which are actually dynamic in real-world systems. The dynamic characteristic of PPI networks is essential for understanding the evolution and regulation mechanism at the molecular level and there is still much room to improve the alignment quality in dynamic networks. RESULTS: In this paper, we proposed a novel alignment algorithm, Twadn, to align dynamic PPI networks based on a strategy of time warping. We compare Twadn with the existing dynamic network alignment algorithm DynaMAGNA++ and DynaWAVE and use area under the receiver operating characteristic curve and area under the precision-recall curve as evaluation indicators. The experimental results show that Twadn is superior to DynaMAGNA++ and DynaWAVE. In addition, we use protein interaction network of Drosophila to compare Twadn and the static network alignment algorithm NetCoffee2 and experimental results show that Twadn is able to capture timing information compared to NetCoffee2. CONCLUSIONS: Twadn is a versatile and efficient alignment tool that can be applied to dynamic network. Hopefully, its application can benefit the research community in the fields of molecular function and evolution. BioMed Central 2020-09-17 /pmc/articles/PMC7495832/ /pubmed/32938373 http://dx.doi.org/10.1186/s12859-020-03672-6 Text en © The Author(s) 2020 Open Access This 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 Research
Zhong, Yuanke
Li, Jing
He, Junhao
Gao, Yiqun
Liu, Jie
Wang, Jingru
Shang, Xuequn
Hu, Jialu
Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks
title Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks
title_full Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks
title_fullStr Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks
title_full_unstemmed Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks
title_short Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks
title_sort twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7495832/
https://www.ncbi.nlm.nih.gov/pubmed/32938373
http://dx.doi.org/10.1186/s12859-020-03672-6
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