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使用底细菌16S rRNA分类训练的机器学习模型预测海洋冷沉积物中的碳化合物存在

Rohan Khan¹, Tulika Bhardwaj¹, Carmen Li¹

¹Geomicrobiology Group, Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada.

Microbiology spectrum

|August 20, 2025

概括

机器学习模型使用微生物DNA准确预测海洋沉积物中的碳化合物存在. 在综合数据上训练的模型显示出在漏现场预测中更广泛的应用潜力.

科学领域:

海洋微生物学和地球化学
生物信息学和机器学习应用
生物圈与地球圈的接口研究

背景情况:

碳化合物透会影响海洋底层微生物群.
微生物群体的组成可以表明碳化合物的存在.
预测碳化合物泄漏点对于勘探和环境监测至关重要.

研究的目的:

使用16S rRNA基因扩增数据测试机器学习模型,预测海洋沉积物中的碳化合物存在.
鉴定不同海洋盆地中碳化合物透的微生物种类.
在地理上不同的位置评估通用预测模型的可行性.

主要方法:

使用377个海洋沉积物样本的16S rRNA基因扩增序列数据.
使用H2O的AutoML平台来训练和比较机器学习模型,重点是梯度增强机器.
分析特征重要性得分,以确定碳化合物存在的诊断微生物种群.

主要成果:

梯度提升机实现了碳化合物存在的最高预测准确性.
特定的微生物分类,包括墨西哥湾的*Aminicenantia*和*Campylobacterota*,以及苏格兰山坡的*Campylobacterota*和JS1群,是关键指标.
模型在盆地内显示出高精度,但在跨盆地预测中精度降低;然而,与特征选择相结合的模型提高了性能.

结论:

通过机器学习分析的微生物分类学显示出预测碳化合物透地点的巨大潜力.
虽然存在盆地特定的微生物群落,但通过仔细的特征选择,一般化模型是可行的.
这种跨学科的方法结合了生物信息学和地球化学,为了解海洋和地质现象提供了强大的工具.

关键词:

16S rRNA 的使用环境微生物学碳化合物机器学习海洋的冷透

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