CytoKavosh: a cytoscape plug-in for finding network motifs in large biological networks.

Network motifs are small connected sub-graphs that have recently gathered much attention to discover structural behaviors of large and complex networks. Finding motifs with any size is one of the most important problems in complex and large networks. It needs fast and reliable algorithms and tools f...

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Autores principales: Ali Masoudi-Nejad, Mitra Ansariola, Zahra Razaghi Moghadam Kashani, Ali Salehzadeh-Yazdi, Sahand Khakabimamaghani
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Publicado: Public Library of Science (PLoS) 2012
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Acceso en línea:https://doaj.org/article/46d1cfd66943456b959a34f4aeab0601
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spelling oai:doaj.org-article:46d1cfd66943456b959a34f4aeab06012021-11-18T07:07:12ZCytoKavosh: a cytoscape plug-in for finding network motifs in large biological networks.1932-620310.1371/journal.pone.0043287https://doaj.org/article/46d1cfd66943456b959a34f4aeab06012012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22952659/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Network motifs are small connected sub-graphs that have recently gathered much attention to discover structural behaviors of large and complex networks. Finding motifs with any size is one of the most important problems in complex and large networks. It needs fast and reliable algorithms and tools for achieving this purpose. CytoKavosh is one of the best choices for finding motifs with any given size in any complex network. It relies on a fast algorithm, Kavosh, which makes it faster than other existing tools. Kavosh algorithm applies some well known algorithmic features and includes tricky aspects, which make it an efficient algorithm in this field. CytoKavosh is a Cytoscape plug-in which supports us in finding motifs of given size in a network that is formerly loaded into the Cytoscape work-space (directed or undirected). High performance of CytoKavosh is achieved by dynamically linking highly optimized functions of Kavosh's C++ to the Cytoscape Java program, which makes this plug-in suitable for analyzing large biological networks. Some significant attributes of CytoKavosh is efficiency in time usage and memory and having no limitation related to the implementation in motif size. CytoKavosh is implemented in a visual environment Cytoscape that is convenient for the users to interact and create visual options to analyze the structural behavior of a network. This plug-in can work on any given network and is very simple to use and generates graphical results of discovered motifs with any required details. There is no specific Cytoscape plug-in, specific for finding the network motifs, based on original concept. So, we have introduced for the first time, CytoKavosh as the first plug-in, and we hope that this plug-in can be improved to cover other options to make it the best motif-analyzing tool.Ali Masoudi-NejadMitra AnsariolaZahra Razaghi Moghadam KashaniAli Salehzadeh-YazdiSahand KhakabimamaghaniPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 8, p e43287 (2012)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Ali Masoudi-Nejad
Mitra Ansariola
Zahra Razaghi Moghadam Kashani
Ali Salehzadeh-Yazdi
Sahand Khakabimamaghani
CytoKavosh: a cytoscape plug-in for finding network motifs in large biological networks.
description Network motifs are small connected sub-graphs that have recently gathered much attention to discover structural behaviors of large and complex networks. Finding motifs with any size is one of the most important problems in complex and large networks. It needs fast and reliable algorithms and tools for achieving this purpose. CytoKavosh is one of the best choices for finding motifs with any given size in any complex network. It relies on a fast algorithm, Kavosh, which makes it faster than other existing tools. Kavosh algorithm applies some well known algorithmic features and includes tricky aspects, which make it an efficient algorithm in this field. CytoKavosh is a Cytoscape plug-in which supports us in finding motifs of given size in a network that is formerly loaded into the Cytoscape work-space (directed or undirected). High performance of CytoKavosh is achieved by dynamically linking highly optimized functions of Kavosh's C++ to the Cytoscape Java program, which makes this plug-in suitable for analyzing large biological networks. Some significant attributes of CytoKavosh is efficiency in time usage and memory and having no limitation related to the implementation in motif size. CytoKavosh is implemented in a visual environment Cytoscape that is convenient for the users to interact and create visual options to analyze the structural behavior of a network. This plug-in can work on any given network and is very simple to use and generates graphical results of discovered motifs with any required details. There is no specific Cytoscape plug-in, specific for finding the network motifs, based on original concept. So, we have introduced for the first time, CytoKavosh as the first plug-in, and we hope that this plug-in can be improved to cover other options to make it the best motif-analyzing tool.
format article
author Ali Masoudi-Nejad
Mitra Ansariola
Zahra Razaghi Moghadam Kashani
Ali Salehzadeh-Yazdi
Sahand Khakabimamaghani
author_facet Ali Masoudi-Nejad
Mitra Ansariola
Zahra Razaghi Moghadam Kashani
Ali Salehzadeh-Yazdi
Sahand Khakabimamaghani
author_sort Ali Masoudi-Nejad
title CytoKavosh: a cytoscape plug-in for finding network motifs in large biological networks.
title_short CytoKavosh: a cytoscape plug-in for finding network motifs in large biological networks.
title_full CytoKavosh: a cytoscape plug-in for finding network motifs in large biological networks.
title_fullStr CytoKavosh: a cytoscape plug-in for finding network motifs in large biological networks.
title_full_unstemmed CytoKavosh: a cytoscape plug-in for finding network motifs in large biological networks.
title_sort cytokavosh: a cytoscape plug-in for finding network motifs in large biological networks.
publisher Public Library of Science (PLoS)
publishDate 2012
url https://doaj.org/article/46d1cfd66943456b959a34f4aeab0601
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