Learning binary threshold networks for gene regulatory network modeling

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665484626
DOIs
StatePublished - 2022
Externally publishedYes
Event2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022 - Ottawa, Canada
Duration: 14 Aug 202216 Aug 2022

Publication series

Name2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022

Conference

Conference2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022
Country/TerritoryCanada
CityOttawa
Period14/08/2216/08/22

Keywords

  • Binary threshold networks
  • Differential evolution
  • Gene regulatory networks
  • Particle swarm optimization

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