Landín M - Search Results

1

Using machine learning to link the influence of transferred Agrobacterium rhizogenes genes to the hormone profile and morphological traits in Centella asiatica hairy roots.

Alcalde MA, Müller M, Munné-Bosch S, Landín M, Gallego PP, Bonfill M, Palazon J, Hidalgo-Martinez D. Alcalde MA, et al. Among authors: landin m. Front Plant Sci. 2022 Sep 2;13:1001023. doi: 10.3389/fpls.2022.1001023. eCollection 2022. Front Plant Sci. 2022. PMID: 36119596 Free PMC article.

2

Artificial neural networks as an alternative to the traditional statistical methodology in plant research.

Gago J, Martínez-Núñez L, Landín M, Gallego PP. Gago J, et al. Among authors: landin m. J Plant Physiol. 2010 Jan 1;167(1):23-7. doi: 10.1016/j.jplph.2009.07.007. Epub 2009 Aug 28. J Plant Physiol. 2010. PMID: 19716625

3

Strengths of artificial neural networks in modeling complex plant processes.

Gago J, Landín M, Gallego PP. Gago J, et al. Among authors: landin m. Plant Signal Behav. 2010 Jun;5(6):743-5. doi: 10.4161/psb.5.6.11702. Epub 2010 Jun 1. Plant Signal Behav. 2010. PMID: 20421726 Free PMC article.

4

Artificial neural networks modeling the in vitro rhizogenesis and acclimatization of Vitis vinifera L.

Gago J, Landín M, Gallego PP. Gago J, et al. Among authors: landin m. J Plant Physiol. 2010 Oct 15;167(15):1226-31. doi: 10.1016/j.jplph.2010.04.008. Epub 2010 Jun 12. J Plant Physiol. 2010. PMID: 20542352

5

Improving knowledge of plant tissue culture and media formulation by neurofuzzy logic: a practical case of data mining using apricot databases.

Gago J, Pérez-Tornero O, Landín M, Burgos L, Gallego PP. Gago J, et al. Among authors: landin m. J Plant Physiol. 2011 Oct 15;168(15):1858-65. doi: 10.1016/j.jplph.2011.04.008. Epub 2011 Jun 14. J Plant Physiol. 2011. PMID: 21676490

6

Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.

Gago J, Martínez-Núñez L, Landín M, Flexas J, Gallego PP. Gago J, et al. Among authors: landin m. PLoS One. 2014 Jan 20;9(1):e85989. doi: 10.1371/journal.pone.0085989. eCollection 2014. PLoS One. 2014. PMID: 24465829 Free PMC article.

7

Computer-Assisted Recovery of Threatened Plants: Keys for Breaking Seed Dormancy of Eryngium viviparum.

Ayuso M, Ramil-Rego P, Landin M, Gallego PP, Barreal ME. Ayuso M, et al. Among authors: landin m. Front Plant Sci. 2017 Dec 12;8:2092. doi: 10.3389/fpls.2017.02092. eCollection 2017. Front Plant Sci. 2017. PMID: 29312370 Free PMC article.

8

Combining DOE With Neurofuzzy Logic for Healthy Mineral Nutrition of Pistachio Rootstocks in vitro Culture.

Nezami-Alanagh E, Garoosi GA, Landín M, Gallego PP. Nezami-Alanagh E, et al. Among authors: landin m. Front Plant Sci. 2018 Oct 15;9:1474. doi: 10.3389/fpls.2018.01474. eCollection 2018. Front Plant Sci. 2018. PMID: 30374362 Free PMC article.

9

Computer-based tools provide new insight into the key factors that cause physiological disorders of pistachio rootstocks cultured in vitro.

Nezami-Alanagh E, Garoosi GA, Landín M, Gallego PP. Nezami-Alanagh E, et al. Among authors: landin m. Sci Rep. 2019 Jul 5;9(1):9740. doi: 10.1038/s41598-019-46155-2. Sci Rep. 2019. PMID: 31278299 Free PMC article.

10

Combining Medicinal Plant In Vitro Culture with Machine Learning Technologies for Maximizing the Production of Phenolic Compounds.

García-Pérez P, Lozano-Milo E, Landín M, Gallego PP. García-Pérez P, et al. Among authors: landin m. Antioxidants (Basel). 2020 Mar 4;9(3):210. doi: 10.3390/antiox9030210. Antioxidants (Basel). 2020. PMID: 32143282 Free PMC article.

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