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相关概念视频

Neural Regulation01:37

Neural Regulation

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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相关实验视频

Updated: Jul 18, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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DeepAmes:一个基于深度学习的Ames测试预测模型,有可能适用于监管应用.

Ting Li1, Zhichao Liu1, Shraddha Thakkar2

  • 1National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.

Regulatory toxicology and pharmacology : RTP
|August 26, 2023
PubMed
概括
此摘要是机器生成的。

DeepAmes是一种新的深度学习模型,可以准确地预测Ames测试结果,以评估突变性. 这种强大的in silico方法在监管科学中为评估消费者产品安全提供了潜在的实用性.

关键词:
艾姆斯试验的测试适用性领域 适用性领域使用上下文.深度学习是一种深度学习.机器学习是机器学习.致变性 致变性 致变性在QSAR中使用QSAR.

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科学领域:

  • 毒理学 毒理学 毒理学
  • 计算化学的计算化学
  • 监管科学 监管科学

背景情况:

  • 艾姆斯试验是评估突变性潜力的全球监管要求.
  • 包括机器学习在内的in silico方法越来越多地用于预测Ames测试结果.
  • 现有的预测模型需要进一步改进,以确保可靠的监管应用.

研究的目的:

  • 开发和验证DeepAmes,这是一种用于预测Ames测试结果的新型深度学习模型.
  • 将DeepAmes的性能与标准机器学习方法进行评估.
  • 评估DeepAmes在监管科学中的实用性,包括其适用性领域.

主要方法:

  • 使用大型的Ames数据集 (>10,000个化合物) 开发一个深度学习模型 (DeepAmes).
  • 将DeepAmes与五种标准机器学习方法进行比较.
  • 模型性能,稳定性和适用性领域的评估.

主要成果:

  • 在对1543种化合物的测试组中,DeepAmes在预测Ames测试结果方面表现出卓越的表现.
  • 该模型即使与其适用范围以外的化合物 (>30%) 保持稳定的性能.
  • 经过修订的DeepAmes版本显示,对于监管使用,敏感度显著提高 (0.87从0.47).

结论:

  • DeepAmes为Ames测试结果提供了一个高性能,深度学习驱动的预测模型.
  • 该模型的定义适用领域和性能特征支持其在监管应用中的潜力.
  • 在消费者产品安全评估中,DeepAmes提供了一种有价值的工具,用于在体中进行突变致死性评估.