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相关概念视频

Mutagenicity and Carcinogenicity01:25

Mutagenicity and Carcinogenicity

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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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Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
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相关实验视频

Updated: Sep 19, 2025

The Lambda Select cII Mutation Detection System
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The Lambda Select cII Mutation Detection System

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开发基于机器学习的模型,用于对非糖甜味剂的应用,进行突变性预测.

Shilpayan Ghosh1, Vinay Kumar1, Kunal Roy1

  • 1Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India.

Molecular informatics
|June 16, 2025
PubMed
概括

机器学习模型预测了人工甜味剂的突变性,为体内试验提供了更快,更具成本效益的替代方案. 六种化合物被优先考虑为潜在的致病性非糖甜味剂 (NSS).

科学领域:

  • 计算化学是一种计算化学.
  • 毒理学 毒理学 毒理学
  • 食品安全科学 食品安全科学

背景情况:

  • 人工甜味剂,或非糖甜味剂 (NSSs),自第二次世界大战以来一直在使用.
  • 有关NSS的突变性潜力存在担忧,需要对新化学品注册进行安全评估.
  • 传统的体内突变性检测是耗时且昂贵的.

研究的目的:

  • 开发和验证机器学习 (ML) 模型,用于预测NSSs的突变性.
  • 为实验性突变性测试提供一个更有效的替代方案.
  • 为了确定潜在的突变性NSS进行进一步的研究.

主要方法:

  • 开发ML模型使用6881个有机化合物的数据集与随机数据分割 (50/50).
  • 交叉验证分析来选择表现最佳的ML模型.
  • 对332个NSS的共识预测,使用六个选定的模型,包括适用性领域评估.
  • 与k-最近邻居的比较和毒性估计软件的预测.

主要成果:

  • 根据严格的交叉验证,选择了六种ML模型.
  • 在共识预测中,六种化合物被确定为潜在的突变性NSS.
  • 与其他计算方法相比,模型衍生预测显示出可靠性.
关键词:
科恩的死亡是科恩的死亡.马修斯相关系数的相关系数沙普利添加剂的解释机器学习是机器学习.其他非糖类甜味剂.量化结构与活动的关系.随机的森林随机的森林

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Assessment of Chemical Toxicity in Adult Drosophila Melanogaster
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结论:

  • 机器学习模型为预测NSS变异性提供了一种可行和有效的方法.
  • 该研究成功地优先考虑了六种化合物作为潜在的突变性NSSs.
  • 已开发的模型可供公众使用,以协助食品安全评估.