预测与雌激素,雌激素和甲状腺激素 (EAT) 相关的内分泌干扰化学物质,使用转录学数据和机器学习
Guillaume Ollitrault1, Marco Marzo2, Alessandra Roncaglioni2
1Inserm U1133, CNRS UMR 8251, Université Paris Cité, 75013 Paris, France.
Toxics
|August 28, 2024
概括
新的in silico模型预测内分泌干扰化学物质 (EDCs) 干扰雌激素,雄激素和甲状腺激素通路. 这些模型有助于快速进行毒理学评估,以保护公众健康.
科学领域:
- 毒理学和药理学的毒理学和药理学.
- 计算化学是一种计算化学.
- 基因组学就是基因组学.
背景情况:
- 内分泌干扰化学物质 (EDC) 构成重大公共健康风险,与癌症,肥胖,糖尿病和不孕症有关.
- 内分泌系统的雌激素 (E),雌激素 (A) 和甲状腺激素 (T) 途径是关键的平稳调节者.
- 需要精确的in silico工具来快速识别EDC及其作用机制.
研究的目的:
- 开发和验证定性基因表达活动关系 (QGexAR) 模型,用于预测EDC诱导的EAT模式的破坏.
- 通过使用多样化的特征选择和机器学习算法优化预测协议.
- 为了确定参与EDC扰动机制的关键基因.
主要方法:
- 利用了来自LINCS数据库的MCF7和A549细胞系的基因表达特征.
- 实现并比较各种分类算法,包括CATBoost,XGBoost,SVM和深度学习.
- 采用基于每个细胞系和终点的最佳表现模型的共识预测策略.
主要成果:
- 开发了雌激素受体,雄激素受体结合和甲状腺激素受体对抗作用的预测模型.
- 在验证组中达成共识平衡的准确度,范围从0.725到0.840.
- 确定了重要的预测特征,突出了已知的特征,并表明了EAT扰乱中的新基因.
结论:
- QGexAR模型提供了一个有效的in silico方法来预测内分泌干扰.
- 开发的模型有助于快速对化学品进行毒理学评估.
- 特性重要性分析为内分泌干扰的分子机制提供了洞察力.
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