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Article

Machine Learning Technology Reveals the Concealed Interactions of Phytohormones on Medicinal Plant In Vitro Organogenesis

1
Applied Plant & Soil Biology, Plant Biology and Soil Science Department, Biology Faculty, University of Vigo, E-36310 Vigo, Spain
2
CITACA—Agri-Food Research and Transfer Cluster, University of Vigo, E-32004 Ourense, Spain
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Pharmacology, Pharmacy and Pharmaceutical Technology Department, Faculty of Pharmacy, University of Santiago, E-15782 Santiago de Compostela, Spain
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Instituto de Investigación Sanitaria de Santiago (IDIS), E-15782 Santiago de Compostela, Spain
*
Author to whom correspondence should be addressed.
Biomolecules 2020, 10(5), 746; https://doi.org/10.3390/biom10050746
Received: 22 April 2020 / Revised: 7 May 2020 / Accepted: 8 May 2020 / Published: 11 May 2020
(This article belongs to the Section Natural and Bio-inspired Molecules)
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. View Full-Text
Keywords: algorithms; artificial intelligence; auxins; cytokinins; in vitro culture; Kalanchoe; plant growth regulators (PGRs); plant tissue culture algorithms; artificial intelligence; auxins; cytokinins; in vitro culture; Kalanchoe; plant growth regulators (PGRs); plant tissue culture
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MDPI and ACS Style

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. https://doi.org/10.3390/biom10050746

AMA Style

García-Pérez P, Lozano-Milo E, Landín M, Gallego PP. Machine Learning Technology Reveals the Concealed Interactions of Phytohormones on Medicinal Plant In Vitro Organogenesis. Biomolecules. 2020; 10(5):746. https://doi.org/10.3390/biom10050746

Chicago/Turabian Style

García-Pérez, Pascual, Eva Lozano-Milo, Mariana Landín, and Pedro P. Gallego. 2020. "Machine Learning Technology Reveals the Concealed Interactions of Phytohormones on Medicinal Plant In Vitro Organogenesis" Biomolecules 10, no. 5: 746. https://doi.org/10.3390/biom10050746

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