Literature DB >> 20542352

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

Jorge Gago1, Mariana Landín, Pedro Pablo Gallego.   

Abstract

This study employs artificial neural networks (ANNs) to create a model to identify relationships between variables affecting the in vitro rhizogenesis and acclimatization of two cultivars of Vitis vinifera L. Albariño and Mencía. The effects of three factors (inputs), the type of cultivar, concentration and exposure time to indolebutyric acid (IBA), on the success of in vitro rhizogenesis and acclimatization were evaluated. The developed model, using ANNs software, was assessed using a separate set of validation data and was in good agreement with the observed results. Exposure time to IBA was found to have the dominant role in influencing the height of acclimatized plantlets. ANNs can be a useful tool for modeling different complex processes and data sets, in plant tissue cultures or, more generally, in plant biology. Copyright (c) 2010 Elsevier GmbH. All rights reserved.

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Year:  2010        PMID: 20542352     DOI: 10.1016/j.jplph.2010.04.008

Source DB:  PubMed          Journal:  J Plant Physiol        ISSN: 0176-1617            Impact factor:   3.549


  10 in total

1.  Artificial Neural Networks Elucidated the Essential Role of Mineral Nutrients versus Vitamins and Plant Growth Regulators in Achieving Healthy Micropropagated Plants.

Authors:  Tomás A Arteta; Radhia Hameg; Mariana Landin; Pedro P Gallego; M Esther Barreal
Journal:  Plants (Basel)       Date:  2022-05-11

2.  Introducing a hybrid artificial intelligence method for high-throughput modeling and optimizing plant tissue culture processes: the establishment of a new embryogenesis medium for chrysanthemum, as a case study.

Authors:  Mohsen Hesami; Roohangiz Naderi; Masoud Tohidfar
Journal:  Appl Microbiol Biotechnol       Date:  2020-10-29       Impact factor: 4.813

3.  Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA).

Authors:  Mohammad M Arab; Abbas Yadollahi; Hamed Ahmadi; Maliheh Eftekhari; Masoud Maleki
Journal:  Front Plant Sci       Date:  2017-11-01       Impact factor: 5.753

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

Authors:  Esmaeil Nezami-Alanagh; Ghasem-Ali Garoosi; Mariana Landín; Pedro Pablo Gallego
Journal:  Front Plant Sci       Date:  2018-10-15       Impact factor: 5.753

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

Authors:  Esmaeil Nezami-Alanagh; Ghasem-Ali Garoosi; Mariana Landín; Pedro Pablo Gallego
Journal:  Sci Rep       Date:  2019-07-05       Impact factor: 4.379

Review 6.  Shoot tip necrosis of in vitro plant cultures: a reappraisal of possible causes and solutions.

Authors:  Jaime A Teixeira da Silva; Esmaeil Nezami-Alanagh; María E Barreal; Mafatlal M Kher; Adhityo Wicaksono; Andrea Gulyás; Norbert Hidvégi; Katalin Magyar-Tábori; Nóra Mendler-Drienyovszki; László Márton; Mariana Landín; Pedro Pablo Gallego; John A Driver; Judit Dobránszki
Journal:  Planta       Date:  2020-09-03       Impact factor: 4.116

7.  Predictive modeling of Persian walnut (Juglans regia L.) in vitro proliferation media using machine learning approaches: a comparative study of ANN, KNN and GEP models.

Authors:  Mohammad Sadat-Hosseini; Mohammad M Arab; Mohammad Soltani; Maliheh Eftekhari; Amanollah Soleimani; Kourosh Vahdati
Journal:  Plant Methods       Date:  2022-04-11       Impact factor: 4.993

8.  Efficient regeneration of mature castanopsis hystrix from in vitro stem explants.

Authors:  Heng Zhang; Mengqing Guo; Qiaona Wu; Mengqiu Zhao; Ruiping Li; Xiaomei Deng; Ruchun Xi
Journal:  Front Plant Sci       Date:  2022-08-12       Impact factor: 6.627

9.  Artificial neural networks modeling gene-environment interaction.

Authors:  Frauke Günther; Iris Pigeot; Karin Bammann
Journal:  BMC Genet       Date:  2012-05-14       Impact factor: 2.797

10.  Modeling and Optimizing a New Culture Medium for In Vitro Rooting of G×N15 Prunus Rootstock using Artificial Neural Network-Genetic Algorithm.

Authors:  Mohammad Mehdi Arab; Abbas Yadollahi; Maliheh Eftekhari; Hamed Ahmadi; Mohammad Akbari; Saadat Sarikhani Khorami
Journal:  Sci Rep       Date:  2018-07-02       Impact factor: 4.379

  10 in total

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