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相关概念视频

In-vitro Mutagenesis01:16

In-vitro Mutagenesis

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To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.
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Mutagenicity and Carcinogenicity01:25

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Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...
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相关实验视频

Updated: Sep 8, 2025

The Lambda Select cII Mutation Detection System
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建模体外变异性使用多任务深度学习和REACH数据.

Panagiotis G Karamertzanis1, Mike Rasenberg1, Imran Shah2

  • 1European Chemicals Agency (ECHA), Telakkakatu 6, Helsinki 00150, Finland.

Chemical research in toxicology
|July 18, 2025
PubMed
概括
此摘要是机器生成的。

这项研究开发了先进的深度学习模型,使用体外测试来预测化学变异性. 多任务模型比单任务方法更准确,提高了基因毒性评估.

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科学领域:

  • 计算毒理学计算毒理学
  • 化学安全评估 化学安全评估
  • 在体外毒理学.

背景情况:

  • 根据REACH法规的变异性评估依赖于体外和体内试验的分层方法.
  • 现有的用于突变性评估的体外检测包括细菌基因突变测试和哺乳动物细胞检测.
  • 探索体外试验试验之间的相关性可能会提高预测模型的性能.

研究的目的:

  • 调查多任务深度学习模型的使用,以基于体外测定数据来预测化学变异性.
  • 将多任务深度学习模型与单任务模型和经典机器学习方法的性能进行比较.
  • 通过使用广泛的外部测试集来评估开发模型的通用性.

主要方法:

  • 从REACH,ToxValDB和文献中编制了一个大型的基因毒性数据集 (>12,000种物质).
  • 开发和评估了各种单任务和多任务深度学习模型,包括图形神经网络.
  • 使用经典机器学习技术和化学指纹进行比较.
  • 为严格的模型验证而构建的外部测试套件.

主要成果:

  • 深度学习单任务模型在体外测试的交叉验证中实现了73-84%的平衡准确性,比经典方法的性能高出2-8%.
  • 细菌特异性基因突变和代谢激活模型显示了82-85%的平衡精度,改善7-12%.
  • 多任务模型对特定测试的交叉验证准确性平均比单任务模型高出8%.
  • 外部验证显示,具有足够数据的最佳模型的精度为72-78%.
  • 图形神经网络嵌入确定了结构性警报和与基因毒性结果相关的结构部分.

结论:

  • 多任务深度学习模型显示出提高体外突变性评估的准确性和效率的前景.
  • 开发的模型可以预测基因毒性并确定结构-活性关系,有助于化学安全评估.
  • 这些计算方法为REACH等监管框架下的传统测试策略提供了有价值的补充.