规范游戏:重新思考精确瘤学药物反应预测的评估
Francesco Codicè1, Corrado Pancotti2, Cesare Rollo2
1Department of Medical Sciences, University of Torino, 10123, Torino, Italy. francesco.codice@unito.it.
Journal of cheminformatics
|March 15, 2025
概括
常见的药物反应预测 (DRP) 验证方法由于数据集偏差而不可靠. 我们提出了一个新的协议,并建议使用剂量反应曲线下的区域进行更准确的DRP模型评估.
科学领域:
- 计算生物学是一种计算生物学.
- 药物基因组学 药物基因组学
- 生物统计学 生物统计学
背景情况:
- 精确瘤学依赖于预测药物反应以进行个性化治疗.
- 癌细胞系药物反应数据集对于临床前研究和机器学习模型开发至关重要.
- 现有的用于药物反应预测 (DRP) 的机器学习模型需要强大的验证以确保可通用性.
研究的目的:
- 确定当前DRP模型评估策略中的局限性.
- 为DRP方法提出一种新的,更可靠的验证协议.
- 解决使用IC50作为预测标签的挑战,并提出替代方案.
主要方法:
- 对影响DRP模型评估的常见数据集偏差的分析.
- 开发一个新的验证协议,包括全球,固定的药物和固定的细胞线聚合策略.
- 将新协议与标准列车测试评估设置集成.
- 审查IC50作为预测标签,并与剂量反应曲线下的面积进行比较.
主要成果:
- 常见的DRP评估策略容易产生数据集偏差,导致"规格游戏"和高估模型性能.
- 拟议的验证协议提供了对DRP模型在多种细胞系和药物中普遍性的更现实的评估.
- 由于它们与药物度范围的相关性,IC50值可能具有误导性,可能导致不准确的性能评估.
结论:
- 现有的DRP验证方法不足以可靠地评估模型的通用性.
- 拟议的聚合策略为评估DRP模型提供了一个更强大的框架.
- 为了更准确的药物反应预测评估,建议用剂量反应曲线下的区域取代IC50.
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