随机森林模型使用高维度质谱数据集准确地分类合成阿片类药物
Kourosh Arasteh1, Steven Magana-Zook2, Colin V Ponce3
1Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. fisher77@llnl.gov.
Analytical methods : advancing methods and applications
|January 7, 2026
概括
机器学习模型,特别是随机森林,使用质谱数据准确检测合成阿片类药物与非阿片类药物. 这促进了复杂混合物中新化学威胁的非定向查.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 法医科学 法医科学 法医科学
背景情况:
- 不针对性地检测未知的化学结构,如新型合成阿片类药物,是一个重大挑战.
- 机器学习 (ML) 为开发用于威胁代理识别的广泛分析方法提供了潜力.
- 现有的方法与潜在的化学威胁的巨大和不断变化的景观作斗争.
研究的目的:
- 开发和验证用于合成阿片类药物的非定向分类的机器学习模型.
- 用质谱数据评估后勤回归和随机森林算法的有效性.
- 为在现场部署可用于新兴威胁检测的分析工具奠定基础.
主要方法:
- 利用了来自数百种合成阿片类药物和非阿片类化合物的名义和高分辨率质谱数据.
- 经过训练和验证的物流回归和随机森林机器学习模型.
- 基于准确性,假阳性和假阴性率来评估模型性能.
主要成果:
- 随机森林模型在将阿片类药物与非阿片类药物分类时实现了超过95%的验证准确性.
- 标称和高分辨率质谱数据都对随机森林有效.
- 开发的随机森林模型准确地预测了以前看不见的化合物的分类.
结论:
- 随机森林模型显示使用质谱测量用于阿片类药物检测的高精度和低错误率.
- 基于机器学习的分析对于开发实用,现场部署的工具来识别新出现的化学威胁至关重要.
- 这种方法支持对含有未知的威胁剂的复杂混合物进行广泛的选.
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