相关实验视频
Updated: Jun 18, 2025

08:15
Metabolomic Analysis of Barley by Gas Chromatography/Mass Spectrometry
Published on: November 8, 2024
396
使用GenPhenML对大麦干旱和盐度应激耐受性的分类和预测
Mahjoubeh Akbari1, Hossein Sabouri1, Sayed Javad Sajadi2
1Department of Plant Production, Collage of Agriculture Science and Natural Resource, Gonbad Kavous University, Gonbad-E Kavus, 4971799151, Iran.
Scientific reports
|July 29, 2024
概括
预测大麦对干旱和盐度等非生物压力的抵抗力对于产量至关重要. 一种新的方法,GenPhenML,使用分子标记和机器学习来准确预测大麦的应力弹性.
科学领域:
- 农业科学 农业科学
- 植物生物学 植物生物学
- 计算生物学 计算生物学
背景情况:
- 全球大麦产量由于遗传和农业学的进步,每年都在增加.
- 无生物应力显著降低大麦产量,需要准确的抵抗预测.
- 人工智能 (AI) 和机器学习 (ML) 为预测作物弹性提供了强大的工具.
研究的目的:
- 为了解决使用分子标记物用于大麦非生物应激预测的研究缺口.
- 介绍GenPhenML,一种结合分子标记和表型特征的新方法,用于预测大麦对干旱和盐度的抵抗力.
- 确定最有效的ML模型来预测大麦的抗应力.
主要方法:
- GenPhenML使用特征选择算法来识别与抗压力相关的关键分子标记.
- 该方法使用ML模型来预测大麦基因型对干旱和盐度压力的耐受性.
- 模型性能使用平均绝对误差 (MAE),根平均平方误差 (RMSE) 和R平方 (R2) 度量来评估.
主要成果:
- 在GenPhenML中的神经网络 (NN) 模型在预测盐度应激抵抗方面取得了很高的准确性 (MAE:0.1206,RMSE:0.0308,R2:0.9995).
- 此外,NN模型在预测干旱应激抵抗性方面也表现出色 (MAE:0.0727,RMSE:0.0105,R2:0.9999).
- 在将大麦基因型分类为耐盐和干旱或敏感的方面,GenPhenML获得了超过97%的准确性,精度和F1评分.
结论:
- 使用ML,GenPhenML有效地整合了分子和表型数据,以预测大麦的抗压力.
- 拟议的方法显著提高了预测大麦对关键非生物压力的适应能力的准确性.
- GenPhenML为加速旨在开发耐压力大麦品种的育种计划提供了有价值的工具.
相关概念视频
Responses to Salt Stress
13.1K
Salt stress—which can be triggered by high salt concentrations in a plant’s environment—can significantly affect plant growth and crop production by influencing photosynthesis and the absorption of water and nutrients.
13.1K
Responses to Drought and Flooding
10.6K
Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.
10.6K
Light Acquisition
8.4K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.4K
Adaptations that Reduce Water Loss
25.4K
Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.
25.4K

