Literature DB >> 20421726

Strengths of artificial neural networks in modeling complex plant processes.

Jorge Gago1, Mariana Landín, Pedro Pablo Gallego.   

Abstract

Commonly, simple mathematical models can not be used to describe exactly the biological processes due to their higher complexity. In fact, most biological interactions cannot be elucidated by a simple stepwise algorithm or a precise formula, particularly when the data are complex or noisy. ANNs allows an accurate description of those kind of biological processes in plant science, offering new advantages over traditional treatments as the possibility of a model, prediction and optimize results. Different kind of data can be analyzed using a unique and "easy to use" technology. Researchers with a high specialized mathematical background are not required and ANNs offer the possibility of achieving the whole view of the experimental study with a limited number of experiments and costs. Additionally, it is possible to add new inputs and outputs to the database to reach a new understanding.

Year:  2010        PMID: 20421726      PMCID: PMC3001577          DOI: 10.4161/psb.5.6.11702

Source DB:  PubMed          Journal:  Plant Signal Behav        ISSN: 1559-2316


  3 in total

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

Authors:  J Gago; L Martínez-Núñez; M Landín; P P Gallego
Journal:  J Plant Physiol       Date:  2009-08-28       Impact factor: 3.549

2.  Advantages of neurofuzzy logic against conventional experimental design and statistical analysis in studying and developing direct compression formulations.

Authors:  Mariana Landín; R C Rowe; P York
Journal:  Eur J Pharm Sci       Date:  2009-08-27       Impact factor: 4.384

3.  Comparison of neural network and multiple linear regression as dissolution predictors.

Authors:  Pradeep M Sathe; Jurgen Venitz
Journal:  Drug Dev Ind Pharm       Date:  2003-03       Impact factor: 3.225

  3 in total
  6 in total

1.  Efficiency of neural network-based combinatorial model predicting optimal culture conditions for maximum biomass yields in hairy root cultures.

Authors:  Shakti Mehrotra; O Prakash; Feroz Khan; A K Kukreja
Journal:  Plant Cell Rep       Date:  2012-11-11       Impact factor: 4.570

2.  Artificial neural network-based model for the prediction of optimal growth and culture conditions for maximum biomass accumulation in multiple shoot cultures of Centella asiatica.

Authors:  Archana Prasad; Om Prakash; Shakti Mehrotra; Feroz Khan; Ajay Kumar Mathur; Archana Mathur
Journal:  Protoplasma       Date:  2016-04-11       Impact factor: 3.356

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

Authors:  Manuel Ayuso; Pablo Ramil-Rego; Mariana Landin; Pedro P Gallego; M Esther Barreal
Journal:  Front Plant Sci       Date:  2017-12-12       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.  Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant Bryophyllum sp. Cultured in vitro.

Authors:  Pascual García-Pérez; Eva Lozano-Milo; Mariana Landin; Pedro Pablo Gallego
Journal:  Front Plant Sci       Date:  2020-12-01       Impact factor: 5.753

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

Authors:  Jorge Gago; Lourdes Martínez-Núñez; Mariana Landín; Jaume Flexas; Pedro P Gallego
Journal:  PLoS One       Date:  2014-01-20       Impact factor: 3.240

  6 in total

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