2022
AEON.py: Python library for attractor analysis in asynchronous Boolean networks
BENEŠ, Nikola; Luboš BRIM; Ondřej HUVAR; Samuel PASTVA; David ŠAFRÁNEK et. al.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
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
References:
Impact factor
Impact factor: 5.800
RIV identification code
RIV/00216224:14330/22:00127100
Organization unit
Faculty of Informatics
UT WoS
000857476100001
EID Scopus
2-s2.0-85141004669
Keywords in English
Boolean Networks; Attractors; Software
Tags
International impact, Reviewed
Changed: 28/3/2023 12:03, RNDr. Pavel Šmerk, Ph.D.
Abstract
In the original language
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 MU |
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| MUNI/G/1771/2020, interní kód MU |
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