NDLI: Choosing an appropriate somatic embryogenesis medium of carrot (Daucus carota L.) by data mining technology

Content Provider	Springer Nature : BioMed Central
Author	Fallah Ziarani, Masoumeh Tohidfar, Masoud Hesami, Mohsen
Abstract	Introduction Developing somatic embryogenesis is one of the main steps in successful in vitro propagation and gene transformation in the carrot. However, somatic embryogenesis is influenced by different intrinsic (genetics, genotype, and explant) and extrinsic (e.g., plant growth regulators (PGRs), medium composition, and gelling agent) factors which cause challenges in developing the somatic embryogenesis protocol. Therefore, optimizing somatic embryogenesis is a tedious, time-consuming, and costly process. Novel data mining approaches through a hybrid of artificial neural networks (ANNs) and optimization algorithms can facilitate modeling and optimizing in vitro culture processes and thereby reduce large experimental treatments and combinations. Carrot is a model plant in genetic engineering works and recombinant drugs, and therefore it is an important plant in research works. Also, in this research, for the first time, embryogenesis in carrot (Daucus carota L.) using Genetic algorithm (GA) and data mining technology has been reviewed and analyzed. Materials and methods In the current study, data mining approach through multilayer perceptron (MLP) and radial basis function (RBF) as two well-known ANNs were employed to model and predict embryogenic callus production in carrot based on eight input variables including carrot cultivars, agar, magnesium sulfate (MgSO4), calcium dichloride (CaCl2), manganese (II) sulfate (MnSO4), 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), and kinetin (KIN). To confirm the reliability and accuracy of the developed model, the result obtained from RBF-GA model were tested in the laboratory. Results The results showed that RBF had better prediction efficiency than MLP. Then, the developed model was linked to a genetic algorithm (GA) to optimize the system. To confirm the reliability and accuracy of the developed model, the result of RBF-GA was experimentally tested in the lab as a validation experiment. The result showed that there was no significant difference between the predicted optimized result and the experimental result. Conclutions Generally, the results of this study suggest that data mining through RBF-GA can be considered as a robust approach, besides experimental methods, to model and optimize in vitro culture systems. According to the RBF-GA result, the highest somatic embryogenesis rate (62.5%) can be obtained from Nantes improved cultivar cultured on medium containing 195.23 mg/l MgSO4, 330.07 mg/l CaCl2, 18.3 mg/l MnSO4, 0.46 mg/l 2,4- D, 0.03 mg/l BAP, and 0.88 mg/l KIN. These results were also confirmed in the laboratory
Related Links	https://bmcbiotechnol.biomedcentral.com/counter/pdf/10.1186/s12896-024-00898-7.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
ISSN	14726750
DOI	10.1186/s12896-024-00898-7
Journal	BMC Biotechnology
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-09-27
Access Restriction	Open
Subject Keyword	Applied Microbiology Biotechnology Biochemical Engineering Genetic Engineering Plant Breeding Biomedical Engineering Artificial neural network Genetic algorithm In vitro culture Machine learning Multilayer perceptron Radial basis function Plant Breeding/Biotechnology Biomedical Engineering/Biotechnology
Content Type	Text
Resource Type	Article
Subject	Biotechnology
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	3.2/2023

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