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dc.contributor.author | García Pérez, Pascual |
dc.contributor.author | Lozano Milo, Eva |
dc.contributor.author | Landín Pérez, Mariana |
dc.contributor.author | Gallego, Pedro Pablo |
dc.date.accessioned | 2020-11-11T11:57:59Z |
dc.date.available | 2020-11-11T11:57:59Z |
dc.date.issued | 2020 |
dc.identifier.citation | García-Pérez, P.; Lozano-Milo, E.; Landín, M.; Gallego, P.P. Machine Learning Technology Reveals the Concealed Interactions of Phytohormones on Medicinal Plant In Vitro Organogenesis. Biomolecules 2020, 10, 746 |
dc.identifier.uri | http://hdl.handle.net/10347/23663 |
dc.description.abstract | Organogenesis constitutes the biological feature driving plant in vitro regeneration, in which the role of plant hormones is crucial. The use of machine learning (ML) technology stands out as a novel approach to characterize the combined role of two phytohormones, the auxin indoleacetic acid (IAA) and the cytokinin 6-benzylaminopurine (BAP), on the in vitro organogenesis of unexploited medicinal plants from the Bryophyllum subgenus. The predictive model generated by neurofuzzy logic, a combination of artificial neural networks (ANNs) and fuzzy logic algorithms, was able to reveal the critical factors affecting such multifactorial process over the experimental dataset collected. The rules obtained along with the model allowed to decipher that BAP had a pleiotropic effect on the Bryophyllum spp., as it caused different organogenetic responses depending on its concentration and the genotype, including direct and indirect shoot organogenesis and callus formation. On the contrary, IAA showed an inhibiting role, restricted to indirect shoot regeneration. In this work, neurofuzzy logic emerged as a cutting-edge method to characterize the mechanism of action of two phytohormones, leading to the optimization of plant tissue culture protocols with high large-scale biotechnological applicability |
dc.description.sponsorship | The authors acknowledge the FPU grant awarded to Pascual García-Pérez from the Spanish Ministry of Education (grant number FPU15/04849) |
dc.language.iso | eng |
dc.publisher | MDPI |
dc.rights | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/) |
dc.rights | Atribución 4.0 Internacional |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ |
dc.subject | Algorithms |
dc.subject | Artificial intelligence |
dc.subject | Auxins |
dc.subject | Cytokinins |
dc.subject | In vitro culture |
dc.subject | Kalanchoe |
dc.subject | Plant growth regulators (PGRs) |
dc.subject | Plant tissue culture |
dc.title | Machine Learning Technology Reveals the Concealed Interactions of Phytohormones on Medicinal Plant In Vitro Organogenesis |
dc.type | journal article |
dc.identifier.doi | 10.3390/biom10050746 |
dc.relation.publisherversion | https://doi.org/10.3390/biom10050746 |
dc.type.hasVersion | VoR |
dc.identifier.essn | 2218-273X |
dc.rights.accessRights | open access |
dc.contributor.affiliation | Universidade de Santiago de Compostela. Departamento de Farmacoloxía, Farmacia e Tecnoloxía Farmacéutica |
dc.description.peerreviewed | SI |
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