释放聚类和分类方法的潜力:导航监督和无监督的化学相似性
Kamel Mansouri1, Kyla Taylor1, Scott Auerbach1
1Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Environmental health perspectives
|August 6, 2024
概括
了解化学相似性对于毒理学新方法方法 (NAMs) 至关重要. 本评论阐明了如何定义相似性的影响对精确的化学危险评估的聚类和分类方法.
科学领域:
- 毒理学和计算化学
- 新方法方法论 (NAM) 的开发和应用.
背景情况:
- 毒理学正在推进NAM用于化学毒性预测.
- 基于类的方法,如聚类和分类,对于NAMs至关重要.
- 计算化学,数据可用性和机器学习增强了这些方法.
研究的目的:
- 为了加深对毒理学的基于类的方法的理解.
- 阐明化学相似性 (结构和生物) 在聚类和分类方法 (CCA) 中的作用.
- 突出CCA的细微差别,应用和常见的滥用.
主要方法:
- 分析无监督 (终点不可知) 和监督 (终点特定) 的相似度.
- 检查化学表征和生物活性标签如何影响相似性定义.
- 讨论选择合适的相似性措施对CCA的影响.
主要成果:
- 协同验证的有效性取决于取决于上下文的相似性定义.
- 无监督方法使用终点不可知相似性来发现模式.
- 监督方法需要特定于终点的相似性来进行分类和预测.
结论:
- 对于有毒学中有效的CCA来说,必须仔细选择和理解相似性.
- 在特定的情境中,错误地应用无监督的方法,比如交叉阅读,可能会导致错误.
- 在准确的毒理学评估中,区分终点不可知性和终点特定相似性至关重要.
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