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. |
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@article{2229990, author = {Beneš, Nikola and Brim, Luboš and Huvar, Ondřej and Pastva, Samuel and Šafránek, David and Šmijáková, Eva}, article_location = {UK}, article_number = {21}, doi = {http://dx.doi.org/10.1093/bioinformatics/btac624}, keywords = {Boolean Networks; Attractors; Software}, language = {eng}, issn = {1367-4803}, journal = {BIOINFORMATICS}, title = {AEON.py: Python library for attractor analysis in asynchronous Boolean networks}, url = {https://doi.org/10.1093/bioinformatics/btac624}, volume = {38}, year = {2022} }
TY - JOUR ID - 2229990 AU - Beneš, Nikola - Brim, Luboš - Huvar, Ondřej - Pastva, Samuel - Šafránek, David - Šmijáková, Eva PY - 2022 TI - AEON.py: Python library for attractor analysis in asynchronous Boolean networks JF - BIOINFORMATICS VL - 38 IS - 21 SP - 4978-4980 EP - 4978-4980 PB - OXFORD UNIV PRESS SN - 13674803 KW - Boolean Networks KW - Attractors KW - Software UR - https://doi.org/10.1093/bioinformatics/btac624 N2 - 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. ER -
BENEŠ, Nikola, Luboš BRIM, Ondřej HUVAR, Samuel PASTVA, David ŠAFRÁNEK and Eva ŠMIJÁKOVÁ. AEON.py: Python library for attractor analysis in asynchronous Boolean networks. \textit{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.
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