在基于微生物组的预测中使用高斯过程与微生物社区不相似性的量化不确定性
Asahi Adachi1, Fan Zhang1, Shigehiko Kanaya1,2
1Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma 630-0192, Japan.
Bioinformatics advances
|March 20, 2025
概括
我们开发了一个新的高斯过程 (GP) 模型来量化人类微生物组预测中的不确定性. 这种概率方法提高了基于微生物组的健康和疾病预测在临床环境中的可靠性.
科学领域:
- 微生物组研究的研究.
- 机器学习应用程序 机器学习应用程序
- 计算生物学是一种计算生物学.
背景情况:
- 人类微生物组与宿主健康和疾病密切相关.
- 机器学习模型越来越多地用于从微生物组数据中预测健康状况.
- 量化预测不确定性对于临床应用至关重要,但仍未得到充分发展.
研究的目的:
- 开发人类微生物组的概率预测模型.
- 将微生物社区的差异纳入预测模型.
- 评估模型在量化预测不确定性的表现.
主要方法:
- 开发了一种高斯过程 (GP) 模型,用于微生物社区差异的内核功能.
- 将模型应用于回归任务,包括时间表年龄,体重指数和疾病严重程度.
- 利用公开可用的人类肠道微生物组数据集进行评估.
主要成果:
- 与现有方法相比,开发的GP模型显示出更高的概率预测准确性.
- 模型中的信心水平与经验覆盖率有很强的相关性.
- 不确定性较低的预测与预测错误的减少相对应.
结论:
- 纳入社区不相似性的高斯过程回归模型有效地处理了家族遗传,高维和稀疏的微生物组数据.
- 这项研究为基于微生物组的预测提供了更可靠的框架.
- 这一进步有可能通过微生物组数据改善健康监测和疾病诊断.
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