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相关概念视频

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

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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堆叠的集体基突变性预测模型使用多种模式与图表注意力网络.

Tanya Liyaqat1, Tanvir Ahmad2, Mohammad Kashif2

  • 1Department of Computer Engineering, Jamia Millia Islamia, New Delhi, 110025, India. tanyaliyaqat791@gmail.com.

Medical & biological engineering & computing
|June 5, 2025
PubMed
概括

这项研究提出了一种新的机器学习模型,用于预测致变性,这是癌症发展的关键因素. 多模式方法提高了在开发早期识别潜在有害药物化合物的准确性.

关键词:
药物发现 药物发现图表注意力网络 图表注意力网络多种模式多种方式.突变性 突变性 突变性堆叠的合奏 堆叠的合奏

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科学领域:

  • 计算化学是一种计算化学.
  • 毒理学 毒理学 毒理学
  • 机器学习 机器学习

背景情况:

  • 由于其与遗传突变和癌症的关联,变异性是一个重大问题.
  • 在药物开发过程中,早期检测致变物化合物对于安全性和成本效益至关重要.
  • 当前的计算突变性预测模型通常依赖于单个数据模式.

研究的目的:

  • 开发一种用于突变性预测的新型堆叠组合模型.
  • 整合多种分子数据模式,以提高预测准确度.
  • 增强早期识别突变原体候选药物的能力.

主要方法:

  • 使用一个堆叠集团机器学习模型.
  • 集成多种分子数据模式:SMILES (亚结构,物理化学,几何) 和分子图 (通过图形注意网络的拓特征).
  • 在特征和分类器显著性分析中使用了SHAP (Shapley添加式解释).

主要成果:

  • 与最先进的方法相比,拟议的多式联运模式表现出优越的性能.
  • 在汉森基准数据集上达到95.21%的曲线下的面积 (AUC).
  • 成功识别了有助于突变性预测的重要特征和分类器.

结论:

  • 这种新的堆叠组合模型有效地整合了多样化的分子信息,以准确地预测突变性.
  • 这种方法在早期药物安全性评估方面取得了重大进展.
  • 这些发现对临床医生和计算生物学家在翻译研究中具有重要意义.