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相关概念视频

Plant Breeding and Biotechnology01:59

Plant Breeding and Biotechnology

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Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.
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Recombinant DNA technology called transgenesis is often used to add a foreign gene or remove a detrimental gene from an organism. Such genetically modified organisms are called transgenic organisms.
The first-ever transgenic plant was a tobacco plant developed in 1983 that showed resistance against the tobacco mosaic virus. Since then, many transgenic plants have been developed and commercialized for improving the agricultural, ornamental, and horticultural value of a crop plant. Transgenic...
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Light Acquisition02:16

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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.
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相关实验视频

Updated: Jun 29, 2025

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant&#8211;Environment Interactions
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数据增强增强了植物基因组启用预测.

Osval A Montesinos-López1, Mario Alberto Solis-Camacho1, Leonardo Crespo-Herrera2

  • 1Facultad de Telemática, Universidad de Colima, Colima 28040, Colima, Mexico.

Genes
|March 28, 2024
PubMed
概括
此摘要是机器生成的。

数据增强 (DA) 通过生成合成数据,提高了植物育种中的基因组选择 (GS) 准确性. 这种方法显著改善了对表现最好的线路的预测,尽管整体准确性可能会有所不同.

关键词:
数据增强数据增强基因组选择 基因组选择这是一种新的方法.植物育种 植物育种

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科学领域:

  • 植物育种 植物育种
  • 基因组学就是基因组学.
  • 机器学习 机器学习

背景情况:

  • 基因组选择 (GS) 是植物育种中的一个强大的工具,但在预测准确性方面面临挑战.
  • 影响GS精度的因素需要新的改进方法.

研究的目的:

  • 调查数据增强 (DA) 在提高基因组选择预测准确性的有效性.
  • 在现实世界育种数据集中评估DA对表现最好的植物系的影响.

主要方法:

  • 利用数据增强 (DA) 的深度神经网络来生成用于训练的合成数据.
  • 将DA技术应用于14个现实植物育种数据集,用于基因组选择.
  • 与传统方法相比,DA增强模型的预测性能.

主要成果:

  • 数据增强显著提高了14个数据集中的前20%线的预测准确度.
  • 顶部线的预测性能平均增长是相当大的 (108.4%的NRMSE,107.4%的MAAPE).
  • 在评估整个测试集时观察到更差的表现,这表明专注于精英线预测.

结论:

  • 数据增强是提高基因组选择预测准确性的有效策略,特别是用于识别精英繁殖系.
  • 建议进行进一步的经验验证,以巩固好处,并了解GS中DA的细微差别.
  • 通过改善优质基因型的识别,DA显示出优化植物育种计划的前景.