Learning binary threshold networks for gene regulatory network modeling

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2 Citas (Scopus)

Resumen

Inspired by the resent trend of binary neural net-works, 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. An evolutionary computation approach to learn binary threshold networks is presented. In particular, we consider differential evolution and particle swarm optimization. We test our method by inferring binary threshold networks of a regulatory network of Quorum sensing systems in bacterium Paraburkholderia phytofirmans PsJN. We present results for weights having only 1 and-1 values, and consider different activation thresholds. 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.

Idioma originalInglés
Título de la publicación alojada2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781665484626
DOI
EstadoPublicada - 2022
Publicado de forma externa
Evento2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022 - Ottawa, Canadá
Duración: 14 ago. 202216 ago. 2022

Serie de la publicación

Nombre2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022

Conferencia

Conferencia2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022
País/TerritorioCanadá
CiudadOttawa
Período14/08/2216/08/22

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