BENEŠ, Nikola, Luboš BRIM, Ondřej HUVAR, Samuel PASTVA, David ŠAFRÁNEK and Eva ŠMIJÁKOVÁ. AEON.py: Python library for attractor analysis in asynchronous Boolean networks. BIOINFORMATICS. UK: OXFORD UNIV PRESS, 2022, vol. 38, No 21, p. 4978-4980. ISSN 1367-4803. Available from: https://dx.doi.org/10.1093/bioinformatics/btac624.
Other formats:   BibTeX LaTeX RIS
Basic information
Original name AEON.py: Python library for attractor analysis in asynchronous Boolean networks
Authors BENEŠ, Nikola (203 Czech Republic, belonging to the institution), Luboš BRIM (203 Czech Republic, belonging to the institution), Ondřej HUVAR (203 Czech Republic, belonging to the institution), Samuel PASTVA (703 Slovakia, belonging to the institution), David ŠAFRÁNEK (203 Czech Republic, guarantor, belonging to the institution) and Eva ŠMIJÁKOVÁ (703 Slovakia, belonging to the institution).
Edition BIOINFORMATICS, UK, OXFORD UNIV PRESS, 2022, 1367-4803.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United Kingdom of Great Britain and Northern Ireland
Confidentiality degree is not subject to a state or trade secret
WWW URL
Impact factor Impact factor: 5.800
RIV identification code RIV/00216224:14330/22:00127100
Organization unit Faculty of Informatics
Doi http://dx.doi.org/10.1093/bioinformatics/btac624
UT WoS 000857476100001
Keywords in English Boolean Networks; Attractors; Software
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 28/3/2023 12:03.
Abstract
AEON.py is a Python library for the analysis of the long-term behaviour in very large asynchronous Boolean networks. It provides significant computational improvements over the state-of-the-art methods for attractor detection. Furthermore, it admits the analysis of partially specified Boolean networks with uncertain update functions. It also includes techniques for identifying viable source-target control strategies and the assessment of their robustness with respect to parameter perturbations.
Links
MUNI/A/1145/2021, interní kód MUName: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace XI. (Acronym: SV-FI MAV XI.)
Investor: Masaryk University
MUNI/G/1771/2020, interní kód MUName: Computational reconstruction of mechanistic framework underlying receptor tyrosine kinase function in signal transduction (Acronym: FGFSIGMOD)
Investor: Masaryk University, INTERDISCIPLINARY - Interdisciplinary research projects
PrintDisplayed: 17/9/2024 14:58