BNClassifier: Classifying Boolean Models by Dynamic Properties

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Authors	BENEŠ Nikola BRIM Luboš HUVAR Ondřej PASTVA Samuel ŠAFRÁNEK David
Year of publication	2024
Type	Article in Proceedings
Conference	Computational Methods in Systems Biology
MU Faculty or unit	Faculty of Informatics
Citation
Web	https://link.springer.com/chapter/10.1007/978-3-031-71671-3_2
Doi	http://dx.doi.org/10.1007/978-3-031-71671-3_2
Keywords	Boolean Network; Model Checking; Hybrid Logic
Attached files	cmsb2024.pdf
Description	Partially Specified Boolean Networks (PSBNs) represent a family of Boolean models resulting from possible interpretations of unknown update logics. Hybrid extension of CTL (HCTL) has the power to express complex dynamical phenomena, such as oscillations or stability. We present BNClassifier to classify Boolean Networks corresponding to a given PSBN according to criteria specified in HCTL. The implementation of the tool is fully symbolic (based on BDDs). The results are visualized using the machine-learning-based technology of decision trees.
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