利用机器学习预测模型评估桃金娘基因型的镉胁迫恢复能力:体外比较分析

IF 3.1 3区 农林科学 Q1 HORTICULTURE
Mehmet Tütüncü, Musab A. Isak, Tolga İzgü, Dicle Dönmez, Yıldız Aka Kaçar, Özhan Şimşek
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引用次数: 0

摘要

本研究调查了镉胁迫在受控离体条件下对两种果实颜色不同的桃金娘(Myrtus communis L.)基因型的微繁殖和生根动态的影响。我们评估了这些基因型对不同浓度镉(0、100、200、300、400 和 500 µM)的反应,以确定剂量对小植株繁殖和根形成的影响。结果表明,在所有浓度下,白果(WF)基因型都比黑果(BF)基因型表现出更强的抗逆性,能保持更高的繁殖率和芽高。例如,在 100 µM Cd 浓度下,白果基因型的繁殖率最高,为 6.73;而在 500 µM Cd 浓度下,黑果基因型的繁殖率最低,为 1.94。同样,镉含量的增加会显著降低两种基因型的根长和根数,这说明了镉对根系发育的不利影响。此外,本研究还采用了机器学习(ML)模型来预测生长结果。多层感知器(MLP)模型包括随机森林(RF)和 XGBoost,用于分析数据。MLP 模型表现出色,展示了先进计算工具在准确预测植物对环境压力的反应方面的潜力。例如,MLP 模型预测芽高的 R2 值为 0.87,预测根长的 R2 值为 0.99,显示出较高的预测准确性。总之,我们的研究结果为了解镉耐受性的基因型差异以及 ML 模型在植物科学中的实用性提供了重要启示。这些结果强调了制定有针对性的策略以提高植物在污染环境中的恢复能力的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing Cadmium Stress Resilience in Myrtle Genotypes Using Machine Learning Predictive Models: A Comparative In Vitro Analysis
This study investigated the effects of cadmium (Cd) stress on the micropropagation and rooting dynamics of two myrtle (Myrtus communis L.) genotypes with different fruit colors under controlled in vitro conditions. We evaluated the response of these genotypes to varying concentrations of Cd (0, 100, 200, 300, 400, and 500 µM) to determine dose-dependent effects on plantlet multiplication and root formation. Our results demonstrate that the white-fruited (WF) genotype exhibits greater resilience than the black-fruited (BF) genotype across all concentrations, maintaining higher multiplication rates and shoot heights. For instance, the multiplication rate at 100 µM Cd was highest for WF at 6.73, whereas BF showed the lowest rate of 1.94 at 500 µM. Similarly, increasing Cd levels significantly impaired root length and the number of roots for both genotypes, illustrating the detrimental impact of Cd on root system development. Additionally, this study incorporated machine learning (ML) models to predict growth outcomes. The multilayer perceptron (MLP) model, including random forest (RF) and XGBoost, was used to analyze the data. The MLP model performed notably well, demonstrating the potential of advanced computational tools in accurately predicting plant responses to environmental stress. For example, the MLP model accurately predicted shoot height with an R2 value of 0.87 and root length with an R2 of 0.99, indicating high predictive accuracy. Overall, our findings provide significant insights into the genotypic differences in Cd tolerance and the utility of ML models in plant science. These results underscore the importance of developing targeted strategies to enhance plant resilience in contaminated environments.
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来源期刊
Horticulturae
Horticulturae HORTICULTURE-
CiteScore
3.50
自引率
19.40%
发文量
998
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