在对便秘的交叉试验中,使用贝叶斯统计模型识别益生菌响应者,考虑摄入和效果期间之间的滞后
Shion Hosoda1,2, Yuichiro Nishimoto3, Yohsuke Yamauchi3
1Department of Electrical Engineering and Bioscience, Graduate School of Advanced Science and Engineering, Waseda University, Tokyo, Japan.
Computational and structural biotechnology journal
|November 13, 2023
概括
这项研究引入了一种新的贝叶斯模型,通过计算摄入和效果之间的时间延迟来准确识别对便秘益生菌治疗的响应者. 该模型改进了以前的方法,考虑了这一关键的滞后期.
科学领域:
- 微生物组研究的研究.
- 统计建模 统计建模
- 胃肠病学 胃肠病学
背景情况:
- 像益生菌这样的微生物干预措施对便秘有希望.
- 益生菌的有效性因人而异,需要对应者进行鉴定.
- 对于响应者而言,现有的统计模型不考虑摄入到效果的滞后时间.
研究的目的:
- 开发一个贝叶斯统计模型来估计在益生菌干预中响应者概率.
- 将益生菌摄入量和观察到的影响之间的滞后期纳入模型.
- 提高对特定益生菌有反应的个体识别的准确性.
主要方法:
- 提出了贝叶斯统计模型来估计应答者概率.
- 包括一个参数来解释益生菌的管理和效果开始之间的滞后.
- 使用合成数据集评估模型性能.
主要成果:
- 提出的贝叶斯模型的表现优于没有考虑滞后时间的基本模型.
- 该模型成功地识别了响应者,即使是那些在滞后期具有显著不确定性的响应者.
- 在对微生物干预的响应者评估中证明了更好的准确性.
结论:
- 开发的贝叶斯模型为评估益生菌响应者提供了更准确的方法.
- 考虑到滞后时间对于在基于微生物组的疗法中精确识别响应者至关重要.
- 该模型推进了针对便秘和其他疾病的益生菌治疗的个性化方法.
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