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 (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. SVR-NSGA-II can be employed as a reliable and applicable computational methodology in future plant tissue culture studies.

中文翻译：

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开发基于支持向量机的模型并与人工神经网络进行比较分析，用于模拟植物组织培养程序：植物生长调节剂对菊花体细胞胚胎发生的影响，作为案例研究。


优化体细胞胚胎发生方案可以被视为成功基因转化研究的第一步也是最重要的一步。然而，由于成本和耗时以及该过程的复杂性，通常很难实现优化的胚胎发生方案。因此，有必要使用一种新颖的计算方法，例如机器学习算法来实现此目的。在本研究中，采用两种机器学习算法，包括作为人工神经网络（ANN）的多层感知器（MLP）和支持向量回归（SVR），对菊花的体细胞胚胎发生进行建模，作为案例研究，并比较它们的预测准确性。结果表明，SVR（R2>0.92）比MLP（R2>0.82）具有更好的性能精度。此外，还应用非支配排序遗传算法-II（NSGA-II）对体细胞胚胎发生进行优化，结果显示胚胎发生率最高（99.09%），每个外植体体细胞胚胎数量最多（56.24）。 ) 可以从含有 9.10 μM 2,4-二氯苯氧基乙酸 (2,4-D)、4.70 μM 激动素 (KIN) 和 18.73 μM 硝普钠 (SNP) 的培养基中获得。根据我们的结果，SVR-NSGA-II能够准确地优化菊花的体细胞胚胎发生。 SVR-NSGA-II可以作为未来植物组织培养研究中可靠且适用的计算方法。