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NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families
Comparative network analysis provides effective computational means for gaining novel insights into the structural and functional compositions of biological networks. In recent years, various methods have been developed for biological network alignment, whose main goal is to identify important simil...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6984706/ https://www.ncbi.nlm.nih.gov/pubmed/31986158 http://dx.doi.org/10.1371/journal.pone.0227598 |
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author | Woo, Hyun-Myung Jeong, Hyundoo Yoon, Byung-Jun |
author_facet | Woo, Hyun-Myung Jeong, Hyundoo Yoon, Byung-Jun |
author_sort | Woo, Hyun-Myung |
collection | PubMed |
description | Comparative network analysis provides effective computational means for gaining novel insights into the structural and functional compositions of biological networks. In recent years, various methods have been developed for biological network alignment, whose main goal is to identify important similarities and critical differences between networks in terms of their topology and composition. A major impediment to advancing network alignment techniques has been the lack of gold-standard benchmarks that can be used for accurate and comprehensive performance assessment of such algorithms. The original NAPAbench (network alignment performance assessment benchmark) was developed to address this problem, and it has been widely utilized by many researchers for the development, evaluation, and comparison of novel network alignment techniques. In this work, we introduce NAPAbench 2—a major update of the original NAPAbench that was introduced in 2012. NAPAbench 2 includes a completely redesigned network synthesis algorithm that can generate protein-protein interaction (PPI) network families whose characteristics closely match those of the latest real PPI networks. Furthermore, the network synthesis algorithm comes with an intuitive GUI that allows users to easily generate PPI network families with an arbitrary number of networks of any size, according to a flexible user-defined phylogeny. In addition, NAPAbench 2 provides updated benchmark datasets—created using the redesigned network synthesis algorithm—which can be used for comprehensive performance assessment of network alignment algorithms and their scalability. |
format | Online Article Text |
id | pubmed-6984706 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-69847062020-02-07 NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families Woo, Hyun-Myung Jeong, Hyundoo Yoon, Byung-Jun PLoS One Research Article Comparative network analysis provides effective computational means for gaining novel insights into the structural and functional compositions of biological networks. In recent years, various methods have been developed for biological network alignment, whose main goal is to identify important similarities and critical differences between networks in terms of their topology and composition. A major impediment to advancing network alignment techniques has been the lack of gold-standard benchmarks that can be used for accurate and comprehensive performance assessment of such algorithms. The original NAPAbench (network alignment performance assessment benchmark) was developed to address this problem, and it has been widely utilized by many researchers for the development, evaluation, and comparison of novel network alignment techniques. In this work, we introduce NAPAbench 2—a major update of the original NAPAbench that was introduced in 2012. NAPAbench 2 includes a completely redesigned network synthesis algorithm that can generate protein-protein interaction (PPI) network families whose characteristics closely match those of the latest real PPI networks. Furthermore, the network synthesis algorithm comes with an intuitive GUI that allows users to easily generate PPI network families with an arbitrary number of networks of any size, according to a flexible user-defined phylogeny. In addition, NAPAbench 2 provides updated benchmark datasets—created using the redesigned network synthesis algorithm—which can be used for comprehensive performance assessment of network alignment algorithms and their scalability. Public Library of Science 2020-01-27 /pmc/articles/PMC6984706/ /pubmed/31986158 http://dx.doi.org/10.1371/journal.pone.0227598 Text en © 2020 Woo et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Woo, Hyun-Myung Jeong, Hyundoo Yoon, Byung-Jun NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families |
title | NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families |
title_full | NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families |
title_fullStr | NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families |
title_full_unstemmed | NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families |
title_short | NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families |
title_sort | napabench 2: a network synthesis algorithm for generating realistic protein-protein interaction (ppi) network families |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6984706/ https://www.ncbi.nlm.nih.gov/pubmed/31986158 http://dx.doi.org/10.1371/journal.pone.0227598 |
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