贝叶斯CurveFit:使用贝叶斯推理改善曲线适合早期复合选的曲线
bioRxiv : the preprint server for biology
|November 24, 2025
概括
BayesCurveFit为有限数据的药物发现试验提供了强大的剂量反应曲线适配. 这种贝叶斯框架准确地量化了关系和不确定性,在杂,稀疏的条件下超过了传统方法.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 生物统计学 生物统计学
背景情况:
- 准确的剂量反应量化在药物发现中至关重要.
- 传统的回归方法在早期选试验中与稀疏的数据和高噪声作斗争.
- 现有的贝叶斯方法往往没有针对数据有限,噪音易感的条件进行优化.
研究的目的:
- 介绍BayesCurveFit,这是一个新的贝叶斯框架,用于强大的剂量反应推断.
- 解决早期药物发现查中传统方法的局限性.
- 从有限的实验数据中实现可靠的参数估计和不确定性量化.
主要方法:
- 开发了BayesCurveFit,一个统一的贝叶斯管道,集成校准初始化,随机优化和自适应后端采样.
- 采用概率混合模型和高斯 - 拉普拉斯混合分布用于残余模型.
- 纳入一个贝叶斯的意义度 (后期错误概率) 响应分类.
主要成果:
- 贝叶斯曲线Fit证明了强大的参数估计和不确定性量化,即使只有少数观察.
- 高斯 - 拉普拉斯混合分布增强了与普通最小平方相比异常值的稳定性.
- 模拟研究和真实查基准表明,在有限的样本采集下,BayesCurveFit在恢复剂量反应关系方面超过了最先进的回归方法.
结论:
- BayesCurveFit提供了一个通用和用户友好的贝叶斯框架,用于分析早期查中的剂量反应数据.
- 该框架为响应分类提供可解释的概率信心.
- 这种方法提高了药物发现前选试验的可靠性,使用有限的,杂的数据.
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