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dc.contributor.authorNISHIDA, CYNTIA EICO HAYAMA
dc.contributor.authorReinaldo Bianchi
dc.contributor.authorCOSTA, ANNA HELENA REALI
dc.date.accessioned2021-11-24T16:48:48Z
dc.date.accessioned2023-05-03T20:35:57Z
dc.date.available2021-11-24T16:48:48Z
dc.date.available2023-05-03T20:35:57Z
dc.date.issued2020-05-16
dc.identifier.citationNISHIDA, C. E. H.; BIANCHI, R. DA C.; COSTA, A. H. R. A framework to shift basins of attraction of gene regulatory networks through batch reinforcement learning. ARTIFICIAL INTELLIGENCE IN MEDICINE, v. 107, p. 101853, 2020.
dc.identifier.issn0933-3657
dc.identifier.urihttps://hdl.handle.net/20.500.12032/89095
dc.description.abstractA major challenge in gene regulatory networks (GRN) of biological systems is to discover when and what in-terventions should be applied to shift them to healthy phenotypes. A set of gene activity profiles, called basin ofattraction (BOA), takes this network to a specific phenotype; therefore, a healthy BOA leads the GRN to a healthyphenotype. However, without the complete observability of the genes, it is not possible to identify whether thecurrent BOA is healthy. In this article we investigate external interventions in GRN with partial observabilityaiming to bring it to healthy BOAs. We propose a new batch reinforcement learning method (BRL), called mSFQI,to define intervention strategies based on the probabilities of the gene activity profiles being in healthy BOAs,which are calculated from a set of previous observed experiences. BRL uses approximation functions and re-peated applications of previous experiences to accelerate learning. Results demonstrate that our proposal canquickly shift a partially observable GRN to healthy BOAs, while reducing the number of interventions. In ad-dition, when observability is poor, mSFQI produces better results when the probabilities for a greater amount ofprevious observations are available.
dc.relation.ispartofARTIFICIAL INTELLIGENCE IN MEDICINE
dc.rightsAcesso Restrito
dc.subjectReinforcement learning
dc.subjectGene regulatory network
dc.subjectBasin of attraction
dc.titleA framework to shift basins of attraction of gene regulatory networks through batch reinforcement learningpt_BR
dc.typeArtigopt_BR


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