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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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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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基因组和基于算法的基因组和算法预测风险评估模型用于暴露.

Minyun Jiang1,2, Na Cai1,2, Juan Hu3

  • 1School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.

Frontiers in public health
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PubMed
概括

这项研究确定了像PTX3这样的关键基因对于检测职业暴露至关重要. 机器学习模型有效预测引起的损害,有助于风险评估.

关键词:
暴露于二烯的情况由引起的损伤.生物信息学是一种生物信息学.机器学习是机器学习.职业健康 职业健康 职业健康风险评估 风险评估 风险评估

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科学领域:

  • 生物信息学是一种生物信息学.
  • 基因组学就是基因组学.
  • 机器学习 机器学习

背景情况:

  • 职业暴露对健康构成重大风险.
  • 确定毒性可靠的生物标志物对于早期检测和管理至关重要.

研究的目的:

  • 确定与职业暴露相关的关键风险基因.
  • 开发基因组和基于算法的风险评估预测模型.

主要方法:

  • 在微阵列数据上利用生物信息学和机器学习 (GSE9569,GSE21862).
  • 鉴定了差异表达基因 (DEGs),并分析了受影响的通路.
  • 应用机器学习用于关键基因识别和在人类和小鼠模型中验证的发现.

主要成果:

  • 确定了40个DEGs,主要用于细胞因子信号传递,脂多糖体反应和化学因子通路.
  • 证实NFKB1和PTX3因暴露而显著改变.
  • PTX3已成为引发损伤的潜在诊断生物标志物.

结论:

  • 开发了基于氧化应激标记的预测性风险评估模型.
  • 这些模型成功地区分了暴露于的个人和对照者.