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Author (up) Ruz, G.A.; Goles, E.
Title Gene regulatory networks with binary weights Type
Year 2023 Publication Biosystems Abbreviated Journal Biosystems
Volume 227 Issue Pages 104902
Abstract An evolutionary computation framework to learn binary threshold networks is presented. Inspired by the recent trend of binary neural networks, where weights and activation thresholds are represented using 1 and -1 such that they can be stored in 1-bit instead of full precision, we explore this approach for gene regulatory network modeling. We test our method by inferring binary threshold networks of two regulatory network models: Quorum sensing systems in bacterium Paraburkholderia phytofirmans PsJN and the fission yeast cell-cycle. We considered differential evolution and particle swarm optimization for the simulations. Results for weights having only 1 and -1 values, and different activation thresholds are presented. Full binary threshold networks were found with minimum error (2 bits), whereas when the binary restriction is relaxed for the activation thresholds, networks with 0 bit error were found.
Corporate Author Data Observatrory Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0303-2647 ISBN Medium
Area Expedition Conference
Notes WOS:000986957200001 Approved
Call Number UAI @ alexi.delcanto @ Serial 1783
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