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
Abstract 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 o...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2020-08-01
|
| Series: | Plant Methods |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13007-020-00655-9 |
| _version_ | 1818188254259707904 |
|---|---|
| author | Mohsen Hesami Roohangiz Naderi Masoud Tohidfar Mohsen Yoosefzadeh-Najafabadi |
| author_facet | Mohsen Hesami Roohangiz Naderi Masoud Tohidfar Mohsen Yoosefzadeh-Najafabadi |
| author_sort | Mohsen Hesami |
| collection | DOAJ |
| description | Abstract 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 The results showed that SVR (R2 > 0.92) had better performance accuracy than MLP (R2 > 0.82). Moreover, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was also applied for the optimization of the somatic embryogenesis and the results showed that the highest embryogenesis rate (99.09%) and the maximum number of somatic embryos per explant (56.24) can be obtained from a medium containing 9.10 μM 2,4-dichlorophenoxyacetic acid (2,4-D), 4.70 μM kinetin (KIN), and 18.73 μM sodium nitroprusside (SNP). According to our results, SVR-NSGA-II was able to optimize the chrysanthemum’s somatic embryogenesis accurately. Conclusions SVR-NSGA-II can be employed as a reliable and applicable computational methodology in future plant tissue culture studies. |
| first_indexed | 2024-12-11T23:24:00Z |
| format | Article |
| id | doaj.art-cb093b8bd2274b3b990dbde2c9ad3839 |
| institution | Directory Open Access Journal |
| issn | 1746-4811 |
| language | English |
| last_indexed | 2024-12-11T23:24:00Z |
| publishDate | 2020-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj.art-cb093b8bd2274b3b990dbde2c9ad38392022-12-22T00:46:16ZengBMCPlant Methods1746-48112020-08-0116111510.1186/s13007-020-00655-9Development 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 studyMohsen Hesami0Roohangiz Naderi1Masoud Tohidfar2Mohsen Yoosefzadeh-Najafabadi3Department of Plant Agriculture, University of GuelphDepartment of Horticultural Science, Faculty of Agriculture, University of TehranDepartment of Plant Biotechnology, Faculty of Science and Biotechnology, Shahid Beheshti University, G.C.Department of Plant Agriculture, University of GuelphAbstract 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 The results showed that SVR (R2 > 0.92) had better performance accuracy than MLP (R2 > 0.82). Moreover, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was also applied for the optimization of the somatic embryogenesis and the results showed that the highest embryogenesis rate (99.09%) and the maximum number of somatic embryos per explant (56.24) can be obtained from a medium containing 9.10 μM 2,4-dichlorophenoxyacetic acid (2,4-D), 4.70 μM kinetin (KIN), and 18.73 μM sodium nitroprusside (SNP). According to our results, SVR-NSGA-II was able to optimize the chrysanthemum’s somatic embryogenesis accurately. Conclusions SVR-NSGA-II can be employed as a reliable and applicable computational methodology in future plant tissue culture studies.http://link.springer.com/article/10.1186/s13007-020-00655-9Artificial intelligenceSupport vector regressionMulti-objective optimization algorithmMachine learning algorithmsMultilayer perceptronSomatic embryogenesis |
| spellingShingle | Mohsen Hesami Roohangiz Naderi Masoud Tohidfar Mohsen Yoosefzadeh-Najafabadi 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 Plant Methods Artificial intelligence Support vector regression Multi-objective optimization algorithm Machine learning algorithms Multilayer perceptron Somatic embryogenesis |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Artificial intelligence Support vector regression Multi-objective optimization algorithm Machine learning algorithms Multilayer perceptron Somatic embryogenesis |
| url | http://link.springer.com/article/10.1186/s13007-020-00655-9 |
| work_keys_str_mv | AT mohsenhesami developmentofsupportvectormachinebasedmodelandcomparativeanalysiswithartificialneuralnetworkformodelingtheplanttissuecultureprocedureseffectofplantgrowthregulatorsonsomaticembryogenesisofchrysanthemumasacasestudy AT roohangiznaderi developmentofsupportvectormachinebasedmodelandcomparativeanalysiswithartificialneuralnetworkformodelingtheplanttissuecultureprocedureseffectofplantgrowthregulatorsonsomaticembryogenesisofchrysanthemumasacasestudy AT masoudtohidfar developmentofsupportvectormachinebasedmodelandcomparativeanalysiswithartificialneuralnetworkformodelingtheplanttissuecultureprocedureseffectofplantgrowthregulatorsonsomaticembryogenesisofchrysanthemumasacasestudy AT mohsenyoosefzadehnajafabadi developmentofsupportvectormachinebasedmodelandcomparativeanalysiswithartificialneuralnetworkformodelingtheplanttissuecultureprocedureseffectofplantgrowthregulatorsonsomaticembryogenesisofchrysanthemumasacasestudy |