使用基因信息和元数据集成计算时间序列肠道微生物群的扩散模型
Misato Seki1, Yao-Zhong Zhang1, Seiya Imoto1
1Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan.
Bioinformatics advances
|August 27, 2025
概括
这项研究引入了一个新的计量框架,用于缺少值的时间序列微生物组数据. 扩散模型有效处理缺失的数据,改善肠道微生物社区分析的下游预测任务.
科学领域:
- 微生物组研究
- 基因组数据分析
- 计算生物学
背景情况:
- 肠道微生物群对宿主健康至关重要, 分析其动态变化需要时间序列的基因组数据.
- 这些数据集中缺少的值对准确分析构成重大挑战.
- 了解微生物社区的动态对于健康和疾病研究至关重要.
研究的目的:
- 为缺少值的时间序列微生物组数据制定有效的归算框架.
- 利用扩散模型处理微生物组研究中缺少的基因组数据.
- 通过计算微生物组数据,提高下游预测任务的准确性.
主要方法:
- 开发了一种基于条件分数的扩散模型,其中包含针对微生物组数据的基因组卷积层.
- 该框架在各种缺失数据比率中对16S rRNA和全基因组猎枪测序数据进行了评估.
- 用表式编码方法将主机元数据集成到模型中,以提高归算性能.
主要成果:
- 拟议的方法显著减少了不同缺失数据级别的平均绝对误差.
- 假定数据集改善了下游预测任务的性能,与现有方法相比,在曲线下获得了竞争力或优异的区域.
- 整合主机元数据进一步提高了归算的准确性,特别是在处理较高比例的缺失数据时.
结论:
- 扩散模型为时间序列微生物组数据中缺失的值赋值提供了一个强大的方法.
- 这种框架提高了微生物组数据分析的可靠性,特别是在纵向研究中.
- 开发的方法为研究肠道微生物群中的复杂相互作用提供了有价值的工具.
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