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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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一种机器学习方法,用于利用高通量转录基因化学查数据进行分子启动事件预测.

Joseph L Bundy1, Jesse D Rogers1, Imran Shah1

  • 1Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Dr, Durham, North Carolina 27709, United States.

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PubMed
概括
此摘要是机器生成的。

一个新的机器学习框架 (MIEML) 从RNA测序数据中预测分子启动事件 (MIE),改进化学危险识别并解决化学安全方面的数据挑战.

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科学领域:

  • 毒素基因组学 毒素基因组学
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 高通量RNA测序为化学危害识别提供了潜力,但由于数据的复杂性而面临挑战.
  • 从大型转录基因数据集中提取机制性见解对于化学安全评估至关重要.

研究的目的:

  • 开发和验证一个机器学习框架 (MIEML) 来从转录化学生物活性屏预测分子启动事件 (MIE).
  • 解决生物信息学方法的需要,以利用高维的转录基因数据来实现化学安全.

主要方法:

  • 训练有素的机器学习分类器可以使用暴露于化学物质的MCF-7细胞的基因表达特征来预测MIE激活.
  • 来自RefChemDB数据库的化学-MIE注释的综合转录组数据.
  • 验证了对已知MIE的分类器性能,并将预测应用于大量测试化学品.

主要成果:

  • 与随机模型相比,三个不同的MIE分类器显示了统计学上显著的预测性能 (p ≤ 0.1).
  • 经过验证的分类器准确地预测了培训套件中不包括的化学品的MIE.
  • 对1750种测试化学品的预测显示,与针对关键受体活动的向分子生物测试的结果存在实质性一致.

结论:

  • MIEML框架有效地从大规模的转录基因数据中预测分子启动事件.
  • 这种方法增强了RNA测序对化学危害识别和安全评估的实用性.
  • 展示了一个可行的计算策略,利用转录基因数据填补化学安全信息的空白.