AEON.py: Python library for attractor analysis in asynchronous Boolean networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F22%3A00127100" target="_blank" >RIV/00216224:14330/22:00127100 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1093/bioinformatics/btac624" target="_blank" >https://doi.org/10.1093/bioinformatics/btac624</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1093/bioinformatics/btac624" target="_blank" >10.1093/bioinformatics/btac624</a>
Alternative languages
Result language
angličtina
Original language name
AEON.py: Python library for attractor analysis in asynchronous Boolean networks
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
BIOINFORMATICS
ISSN
1367-4803
e-ISSN
1367-4811
Volume of the periodical
38
Issue of the periodical within the volume
21
Country of publishing house
GB - UNITED KINGDOM
Number of pages
3
Pages from-to
4978-4980
UT code for WoS article
000857476100001
EID of the result in the Scopus database
2-s2.0-85141004669