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相关概念视频

Light Acquisition02:16

Light Acquisition

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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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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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综合转录基因元分析和比较人工智能模型在生物压力下的玉米中.

Leyla Nazari1, Muhammet Fatih Aslan2, Kadir Sabanci2

  • 1Crop and Horticultural Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran. l.nazari@areeo.ac.ir.

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概括

这项研究确定了在生物压力期间升级的关键玉米基因,这对于开发抗病作物至关重要. 机器学习和深度学习模型有效地分类了这些应激反应基因,突出了BiLSTM.

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科学领域:

  • 遗传学和基因组学 在
  • 植物病理学 植物病理学
  • 生物信息学是一种生物信息学.

背景情况:

  • 玉米 (Zea mays) 容易因病原体诱导的生物压力而损失产量.
  • 识别参与疾病耐药性的基因对于开发弹性玉米品种至关重要.
  • 在压力下了解基因表达模式可以提高对玉米防御机制的了解.

研究的目的:

  • 用机器学习和深度学习来分类在正常与生物压力条件下表达的玉米基因.
  • 评估各种算法在识别应激反应基因方面的性能.
  • 为了确定在生物压力期间差异地表达的特定基因.

主要方法:

  • 机器学习算法的应用:天真贝叶斯,K-最近邻居,合集,支持向量机器和决策树.
  • 开发一种深度学习模型,使用具有循环神经网络 (RNN) 架构的双向长期短期记忆 (BiLSTM) 网络.
  • 使用Relief算法进行特征选择,以提高分类性能.

主要成果:

  • 与其他机器学习算法相比,双向长期短期记忆 (BiLSTM) 模型表现出卓越的性能.
  • 几种基因,包括 (S) - 贝塔 - 宏烯合成酶,泽亚莱克辛A1合成酶和与致病性相关的蛋白质10,在生物压力下被确定为差异上调.
  • 浮雕特征选择算法提高了分类模型的有效性.

结论:

  • 深度学习,特别是BiLSTM,提供了一种强大的方法来对玉米中压力反应基因进行分类.
  • 已识别的上调基因为培育抗病玉米品种提供了有价值的标.
  • 这项研究推动了我们对玉米对生物压力的遗传反应的理解,有助于作物改进战略.