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Updated: Jan 16, 2026

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
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Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

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不确定性建模优于微生物组数据分析的机器学习

Maxwell A Konnaris1, Manan Saxena2, Nicole Lazar3

  • 1Program in Bioinformatics and Genomics, Pennsylvania State University, University Park, PA, USA.

bioRxiv : the preprint server for biology
|September 26, 2025
PubMed
概括

微生物组测序缺乏总微生物负载数据. 机器学习模型无法准确预测负载,但贝叶斯方法为微生物组分析提供了可靠的解决方案.

关键词:
在16S rRNA-seqq.交变变量转移的交变变量机器学习 机器学习转基因组学是指转基因组学.微生物组数据 微生物组数据部分识别模型 部分识别模型规模依赖的推理推理序列计数数据 序列计数数据总微生物负荷总量不确定性定量化 不确定性定量化

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