背景:优化体细胞胚发生方案可以被认为是成功的基因转化研究的第一步。然而,由于该过程的成本和耗时以及复杂性,通常难以实现优化的胚胎发生方案。因此,有必要使用一种新的计算方法,例如用于此目的的机器学习算法。在本研究中,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),被用来模拟菊花的体细胞胚胎发生,作为一个案例研究,并比较它们的预测精度。
结果:结果表明,SVR(R2>0.92)比MLP(R2>0.82)具有更好的性能准确性。此外,非支配排序遗传算法-II(NSGA-II)也用于体细胞胚胎发生的优化,结果表明,最高的胚胎发生率(99.09%)和每个外植体的最大体细胞胚胎数(56.24)可以从含有9.10μM2,4-二氯苯氧基乙酸(2,4-D)的培养基中获得,4.70μM激动素(KIN),和18.73μM硝普钠(SNP)。根据我们的结果,SVR-NSGA-II能够准确优化菊花的体细胞胚胎发生。
结论:SVR-NSGA-II可以在未来的植物组织培养研究中用作可靠和适用的计算方法。
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.