通过人工神经网络对内分泌干扰化学物质的二进制分类
Zahir Aghayev1,2, George F Walker1,2, Funda Iseri3,4
1Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT 06269, USA.
概括
本研究介绍了一种机器学习框架,使用人工神经网络 (ANN) 和图像分析来准确预测化学化合物.
科学领域:
- 计算毒理学计算毒理学
- 内分泌学 在内分泌学.
- 在化学安全领域的机器学习应用.
背景情况:
- 内分泌干扰化学物质 (EDC) 通过干扰激素系统,对人类和野生动物健康构成风险.
- 使用传统的体外或体内方法评估大量现有化学品的内分泌干扰潜力是具有挑战性和耗时的.
研究的目的:
- 开发和验证用于预测化学化合物雌激素受体活性的机器学习框架.
- 通过高含量图像分析和人工神经网络,模拟雌激素受体激活剂和对抗剂之间的分离.
主要方法:
- 高内容/高通量图像分析与人工神经网络 (ANN) 的整合.
- 实验数据的预处理,包括清理,缩放和特征工程,重点关注受体-DNA结合值的中间50%.
- 主要组件分析 (PCA) 的应用,以最大限度地减少实验噪声,并增强特征表示用于分类.
主要成果:
- 开发的基于ANN的框架在分类雌激素受体agonists和antagonists方面取得了很高的准确性.
- 该模型在区分基准激动剂和对抗剂化学物质方面显示了98.41%的准确性.
结论:
- 机器学习,特别是ANN与图像分析相结合,提供了一种强大而快速的方法来评估化学毒性和内分泌干扰潜力.
- 这种数据驱动的框架为全面评估化学品安全和识别潜在内分泌干扰剂提供了有前途的工具.
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