Development of support vector machine-based model and comparative analysis with artificial neural network for modeling the plant tissue culture procedures: effect of plant growth regulators on somatic embryogenesis of chrysanthemum, as a case study.
Artificial intelligence
Chrysanthemum
Machine learning algorithms
Multi-objective optimization algorithm
Multilayer perceptron
Nitric oxide
Somatic embryogenesis
Support vector regression
Journal
Plant methods
ISSN: 1746-4811
Titre abrégé: Plant Methods
Pays: England
ID NLM: 101245798
Informations de publication
Date de publication:
2020
2020
Historique:
received:
12
05
2020
accepted:
08
08
2020
entrez:
21
8
2020
pubmed:
21
8
2020
medline:
21
8
2020
Statut:
epublish
Résumé
Optimizing the somatic embryogenesis protocol can be considered as the first and foremost step in successful gene transformation studies. However, it is usually difficult to achieve an optimized embryogenesis protocol due to the cost and time-consuming as well as the complexity of this process. Therefore, it is necessary to use a novel computational approach, such as machine learning algorithms for this aim. In the present study, two machine learning algorithms, including Multilayer Perceptron (MLP) as an artificial neural network (ANN) and support vector regression (SVR), were employed to model somatic embryogenesis of chrysanthemum, as a case study, and compare their prediction accuracy. The results showed that SVR (R SVR-NSGA-II can be employed as a reliable and applicable computational methodology in future plant tissue culture studies.
Sections du résumé
BACKGROUND
BACKGROUND
Optimizing the somatic embryogenesis protocol can be considered as the first and foremost step in successful gene transformation studies. However, it is usually difficult to achieve an optimized embryogenesis protocol due to the cost and time-consuming as well as the complexity of this process. Therefore, it is necessary to use a novel computational approach, such as machine learning algorithms for this aim. In the present study, two machine learning algorithms, including Multilayer Perceptron (MLP) as an artificial neural network (ANN) and support vector regression (SVR), were employed to model somatic embryogenesis of chrysanthemum, as a case study, and compare their prediction accuracy.
RESULTS
RESULTS
The results showed that SVR (R
CONCLUSIONS
CONCLUSIONS
SVR-NSGA-II can be employed as a reliable and applicable computational methodology in future plant tissue culture studies.
Identifiants
pubmed: 32817755
doi: 10.1186/s13007-020-00655-9
pii: 655
pmc: PMC7424974
doi:
Types de publication
Journal Article
Langues
eng
Pagination
112Informations de copyright
© The Author(s) 2020.
Déclaration de conflit d'intérêts
Competing interestsThe authors declare that they have no competing interests.
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