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ANAT 3.0: a framework for elucidating functional protein subnetworks using graph-theoretic and machine learning approaches

BACKGROUND: ANAT is a Cytoscape plugin for the inference of functional protein–protein interaction networks in yeast and human. It is a flexible graphical tool for scientists to explore and elucidate the protein–protein interaction pathways of a process under study. RESULTS: Here we present ANAT3.0,...

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Autores principales: Signorini, L. F., Almozlino, T., Sharan, R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8555137/
https://www.ncbi.nlm.nih.gov/pubmed/34706638
http://dx.doi.org/10.1186/s12859-021-04449-1
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author Signorini, L. F.
Almozlino, T.
Sharan, R.
author_facet Signorini, L. F.
Almozlino, T.
Sharan, R.
author_sort Signorini, L. F.
collection PubMed
description BACKGROUND: ANAT is a Cytoscape plugin for the inference of functional protein–protein interaction networks in yeast and human. It is a flexible graphical tool for scientists to explore and elucidate the protein–protein interaction pathways of a process under study. RESULTS: Here we present ANAT3.0, which comes with updated PPI network databases of 544,455 (human) and 155,504 (yeast) interactions, and a new machine-learning layer for refined network elucidation. Together they improve network reconstruction to more than twofold increase in the quality of reconstructing known signaling pathways from KEGG. CONCLUSIONS: ANAT3.0 includes improved network reconstruction algorithms and more comprehensive protein–protein interaction networks than previous versions. ANAT is available for download on the Cytoscape Appstore and at https://www.cs.tau.ac.il/~bnet/ANAT/.
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spelling pubmed-85551372021-10-29 ANAT 3.0: a framework for elucidating functional protein subnetworks using graph-theoretic and machine learning approaches Signorini, L. F. Almozlino, T. Sharan, R. BMC Bioinformatics Research BACKGROUND: ANAT is a Cytoscape plugin for the inference of functional protein–protein interaction networks in yeast and human. It is a flexible graphical tool for scientists to explore and elucidate the protein–protein interaction pathways of a process under study. RESULTS: Here we present ANAT3.0, which comes with updated PPI network databases of 544,455 (human) and 155,504 (yeast) interactions, and a new machine-learning layer for refined network elucidation. Together they improve network reconstruction to more than twofold increase in the quality of reconstructing known signaling pathways from KEGG. CONCLUSIONS: ANAT3.0 includes improved network reconstruction algorithms and more comprehensive protein–protein interaction networks than previous versions. ANAT is available for download on the Cytoscape Appstore and at https://www.cs.tau.ac.il/~bnet/ANAT/. BioMed Central 2021-10-27 /pmc/articles/PMC8555137/ /pubmed/34706638 http://dx.doi.org/10.1186/s12859-021-04449-1 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 Research
Signorini, L. F.
Almozlino, T.
Sharan, R.
ANAT 3.0: a framework for elucidating functional protein subnetworks using graph-theoretic and machine learning approaches
title ANAT 3.0: a framework for elucidating functional protein subnetworks using graph-theoretic and machine learning approaches
title_full ANAT 3.0: a framework for elucidating functional protein subnetworks using graph-theoretic and machine learning approaches
title_fullStr ANAT 3.0: a framework for elucidating functional protein subnetworks using graph-theoretic and machine learning approaches
title_full_unstemmed ANAT 3.0: a framework for elucidating functional protein subnetworks using graph-theoretic and machine learning approaches
title_short ANAT 3.0: a framework for elucidating functional protein subnetworks using graph-theoretic and machine learning approaches
title_sort anat 3.0: a framework for elucidating functional protein subnetworks using graph-theoretic and machine learning approaches
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8555137/
https://www.ncbi.nlm.nih.gov/pubmed/34706638
http://dx.doi.org/10.1186/s12859-021-04449-1
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