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相关概念视频

X-ray Crystallography02:18

X-ray Crystallography

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The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
Diffraction
Diffraction is the change in the direction of travel experienced by an electromagnetic wave when it encounters a physical barrier whose dimensions are comparable to those of the wavelength of the light. X-rays are electromagnetic radiation with wavelengths about as long as the distance between neighboring...
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X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

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X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
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The de Broglie Wavelength02:32

The de Broglie Wavelength

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In the macroscopic world, objects that are large enough to be seen by the naked eye follow the rules of classical physics. A billiard ball moving on a table will behave like a particle; it will continue traveling in a straight line unless it collides with another ball, or it is acted on by some other force, such as friction. The ball has a well-defined position and velocity or well-defined momentum, p = mv, which is defined by mass m and velocity v at any given moment. This is the typical...
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Fourier-Based Diffraction Analysis of Live Caenorhabditis elegans
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可解释的机器学习用于衍射模式.

Shah Nawaz1, Vahid Rahmani1, David Pennicard1

  • 1Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany.

Journal of applied crystallography
|October 4, 2023
PubMed
概括

卷积神经网络 (CNN) 将串行晶体学数据分类为成功或失败. 这项研究可视化了CNN,揭示了哪些图像特征驱动了这些分类,从而打开了CNN的门.

关键词:
这是Grad-CAM.可以解释的机器学习梯度加权类激活映射映射的梯度加权类激活映射对表示的可视化.

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科学领域:

  • 晶体学 晶体学是指结晶学.
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 序列结晶学实验产生了庞大的数据集,需要有效的数据缩小.
  • 准确分类"命中"和"错过"数据对于X射线自由电子激光实验的下游分析至关重要.
  • 目前用于数据分类的卷积神经网络 (CNN) 缺乏透明度,作为"黑子"运行.

研究的目的:

  • 质量调查用于串行晶体学数据分类的CNN的内部运作.
  • 开发可视化方法,突出显示图像特征,对CNN预测至关重要.
  • 提高深度学习模型在结构生物学数据分析中的可解释性.

主要方法:

  • 使用卷积神经网络 (CNN) 的图像分类技术的应用.
  • 开发和实施可视化方法来解释CNN预测.
  • 在串行结晶学数据集中对"命中"和"错过"分类的特征贡献的定性分析.

主要成果:

  • 通过CNNs成功将串行晶体学数据分为"命中"和"错过"类别.
  • 可视化技术成功地确定了影响分类结果的图像区域.
  • 在CNN架构中展示特定预测的特征重要性.

结论:

  • 这项研究为序列结晶学数据分析提供了对CNN的定性见解.
  • 可视化功能贡献揭开了这些深度学习模型的"黑子"性质.
  • 对CNN的解释性可以提高信心,并指导高通量结构生物学的进一步发展.