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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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Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
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Criteria for Causality: Bradford Hill Criteria - II01:28

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The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
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向着可解释的致癌性预测:一种综合化学信息学方法和对可能致癌化学品的共识框架.

Huynh Anh Duy1,2, Tarapong Srisongkram3

  • 1Graduate School in the Program of Research and Development in Pharmaceuticals, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.

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概括

一个共识机器学习框架准确地预测化学致癌性 (IARC类2B). 该工具有助于识别潜在的致癌物质,改善化学品安全评估.

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科学领域:

  • 计算毒理学计算毒理学
  • 化学信息学 化学信息学
  • 机器学习在药物发现中的作用

背景情况:

  • 致癌性评估对于公共卫生和监管决策至关重要.
  • 化学品的分类,特别是那些属于国际癌症研究机构 (IARC) 2B类 (可能致癌物) 的化学品,带来了重大挑战.
  • 现有的方法可能缺乏大规模化学选所需的准确性和效率.

研究的目的:

  • 开发和验证一个强大的机器学习框架,用于预测化学致癌性.
  • 评估单个模型 (BiLSTM,LightGBM,Random Forest) 的性能,以及对基准和独立数据集的共识模型.
  • 用模型解释性技术识别与致癌性相关的关键分子特征.

主要方法:

  • 使用三个机器学习模型构建了一个共识框架:BiLSTM (MACCS指纹),LightGBM (RDKit描述器) 和随机森林 (E-state特性).
  • 在基准致癌性数据集上训练和评估模型,包括独立的人类致癌性测试集 (IARC,IRIS).
  • 使用SHAP分析来解释BiLSTM模型并确定结构-活动关系.

主要成果:

  • 在基准数据上,LightGBM模型表现出高精度 (0.800) 和AUROC (0.882) 的最高性能.
  • 共识模型在独立测试集上显示出强烈的概括性 (准确率 = 0.753,AUROC = 0.842).
  • 适用于47种IARC第2B类化合物,其中16种被归类为潜在致癌物,8种被认为非致癌物,23种不确定的.
  • SHAP分析确定了与预测致癌性相关的特定MACCS键和Bemis-Murcko支架.

结论:

  • 开发的共识机器学习框架为致癌性评估提供了可靠和准确的方法.
  • 该工具成功地将化学品归类为IARC类2B类别,有助于风险评估.
  • 一个公开可访问的网络服务器可用,以促进这种致癌性预测工具的实际应用.