BENEŠ, Nikola, Luboš BRIM, Samuel PASTVA and David ŠAFRÁNEK. Aeon 2021: Bifurcation Decision Trees in Boolean Networks. In Cinquemani et al. International Conference on Computational Methods in Systems Biology (CMSB 2021). Cham: Springer, 2021, p. 230-237. ISBN 978-3-030-85632-8. Available from: https://dx.doi.org/10.1007/978-3-030-85633-5_14.
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Basic information
Original name Aeon 2021: Bifurcation Decision Trees in Boolean Networks
Authors BENEŠ, Nikola (203 Czech Republic, belonging to the institution), Luboš BRIM (203 Czech Republic, belonging to the institution), Samuel PASTVA (703 Slovakia, belonging to the institution) and David ŠAFRÁNEK (203 Czech Republic, guarantor, belonging to the institution).
Edition Cham, International Conference on Computational Methods in Systems Biology (CMSB 2021), p. 230-237, 8 pp. 2021.
Publisher Springer
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Germany
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
Impact factor Impact factor: 0.402 in 2005
RIV identification code RIV/00216224:14330/21:00124485
Organization unit Faculty of Informatics
ISBN 978-3-030-85632-8
ISSN 0302-9743
Doi http://dx.doi.org/10.1007/978-3-030-85633-5_14
Keywords in English Boolean networks; attractors; software tool
Tags firank_B
Tags International impact, Reviewed
Changed by Changed by: prof. RNDr. Luboš Brim, CSc., učo 197. Changed: 6/1/2023 10:03.
Abstract
Aeon is a recent tool which enables efficient analysis of long-term behaviour of asynchronous Boolean networks with unknown parameters. In this tool paper, we present a novel major release of Aeon (Aeon 2021) which introduces substantial new features compared to the original version. These include (i) enhanced static analysis functionality that verifies integrity of the Boolean network with its regulatory graph; (ii) state-space visualisation of individual attractors; (iii) stability analysis of network variables with respect to parameters; and finally, (iv) a novel decision-tree based interactive visualisation module allowing the exploration of complex relationships between parameters and network behaviour. Aeon 2021 is open-source, fully compatible with SBML-qual models, and available as an online application with an independent native compute engine responsible for resource-intensive tasks. The paper artefact is available via https://doi.org/10.5281/zenodo.5008293.
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