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
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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