Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366245748> ?p ?o ?g. }
Showing items 1 to 96 of
96
with 100 items per page.
- W4366245748 endingPage "104902" @default.
- W4366245748 startingPage "104902" @default.
- W4366245748 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." @default.
- W4366245748 created "2023-04-20" @default.
- W4366245748 creator A5034374719 @default.
- W4366245748 creator A5053436240 @default.
- W4366245748 date "2023-05-01" @default.
- W4366245748 modified "2023-10-17" @default.
- W4366245748 title "Gene regulatory networks with binary weights" @default.
- W4366245748 cites W1595159159 @default.
- W4366245748 cites W1930260923 @default.
- W4366245748 cites W1971224531 @default.
- W4366245748 cites W1982583288 @default.
- W4366245748 cites W1990139847 @default.
- W4366245748 cites W1995341919 @default.
- W4366245748 cites W2006096004 @default.
- W4366245748 cites W2011486153 @default.
- W4366245748 cites W2037385927 @default.
- W4366245748 cites W2051213048 @default.
- W4366245748 cites W2052556966 @default.
- W4366245748 cites W2053961182 @default.
- W4366245748 cites W2063615892 @default.
- W4366245748 cites W2083034544 @default.
- W4366245748 cites W2108114927 @default.
- W4366245748 cites W2109861750 @default.
- W4366245748 cites W2115813074 @default.
- W4366245748 cites W2127405536 @default.
- W4366245748 cites W2128084896 @default.
- W4366245748 cites W2132935366 @default.
- W4366245748 cites W2136559804 @default.
- W4366245748 cites W2137116076 @default.
- W4366245748 cites W2152195021 @default.
- W4366245748 cites W2559439756 @default.
- W4366245748 cites W2617452052 @default.
- W4366245748 cites W2763196980 @default.
- W4366245748 cites W2886991855 @default.
- W4366245748 cites W2897146349 @default.
- W4366245748 cites W2901574078 @default.
- W4366245748 cites W2951404033 @default.
- W4366245748 cites W3008515144 @default.
- W4366245748 cites W3015456223 @default.
- W4366245748 cites W3103145119 @default.
- W4366245748 cites W3104151879 @default.
- W4366245748 cites W4293519307 @default.
- W4366245748 doi "https://doi.org/10.1016/j.biosystems.2023.104902" @default.
- W4366245748 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37080282" @default.
- W4366245748 hasPublicationYear "2023" @default.
- W4366245748 type Work @default.
- W4366245748 citedByCount "0" @default.
- W4366245748 crossrefType "journal-article" @default.
- W4366245748 hasAuthorship W4366245748A5034374719 @default.
- W4366245748 hasAuthorship W4366245748A5053436240 @default.
- W4366245748 hasConcept C104317684 @default.
- W4366245748 hasConcept C11413529 @default.
- W4366245748 hasConcept C150194340 @default.
- W4366245748 hasConcept C186060115 @default.
- W4366245748 hasConcept C33923547 @default.
- W4366245748 hasConcept C41008148 @default.
- W4366245748 hasConcept C48372109 @default.
- W4366245748 hasConcept C54355233 @default.
- W4366245748 hasConcept C67339327 @default.
- W4366245748 hasConcept C74750220 @default.
- W4366245748 hasConcept C85617194 @default.
- W4366245748 hasConcept C86803240 @default.
- W4366245748 hasConcept C94375191 @default.
- W4366245748 hasConceptScore W4366245748C104317684 @default.
- W4366245748 hasConceptScore W4366245748C11413529 @default.
- W4366245748 hasConceptScore W4366245748C150194340 @default.
- W4366245748 hasConceptScore W4366245748C186060115 @default.
- W4366245748 hasConceptScore W4366245748C33923547 @default.
- W4366245748 hasConceptScore W4366245748C41008148 @default.
- W4366245748 hasConceptScore W4366245748C48372109 @default.
- W4366245748 hasConceptScore W4366245748C54355233 @default.
- W4366245748 hasConceptScore W4366245748C67339327 @default.
- W4366245748 hasConceptScore W4366245748C74750220 @default.
- W4366245748 hasConceptScore W4366245748C85617194 @default.
- W4366245748 hasConceptScore W4366245748C86803240 @default.
- W4366245748 hasConceptScore W4366245748C94375191 @default.
- W4366245748 hasLocation W43662457481 @default.
- W4366245748 hasLocation W43662457482 @default.
- W4366245748 hasOpenAccess W4366245748 @default.
- W4366245748 hasPrimaryLocation W43662457481 @default.
- W4366245748 hasRelatedWork W2003904901 @default.
- W4366245748 hasRelatedWork W2023713356 @default.
- W4366245748 hasRelatedWork W2041925009 @default.
- W4366245748 hasRelatedWork W2063773324 @default.
- W4366245748 hasRelatedWork W2098995133 @default.
- W4366245748 hasRelatedWork W2325936523 @default.
- W4366245748 hasRelatedWork W2353543086 @default.
- W4366245748 hasRelatedWork W2770121110 @default.
- W4366245748 hasRelatedWork W2911471039 @default.
- W4366245748 hasRelatedWork W4366245748 @default.
- W4366245748 hasVolume "227-228" @default.
- W4366245748 isParatext "false" @default.
- W4366245748 isRetracted "false" @default.
- W4366245748 workType "article" @default.