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Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
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增强的可解释神经网络方法,用于统一的批量效应缓解和疾病分类,使用交叉队列微生物组配置文件.

Daryl Lx Fung1, Mohd Wasif Khan2, Carson Kai-Sang Leung1

  • 1Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada.

Journal of computational biology : a journal of computational molecular cell biology
|August 8, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的一步方法,同时消除批量效应和分类口腔微生物群疾病. 该方法利用LassoNet与批量损失,准确识别与疾病相关的微生物,改善口腔微生物组研究.

关键词:
拉索网 (LassoNet) 是一个网络.批量效应 批量效应 批量效应交叉队列的一个群体.可以解释的可解释.微生物组是一个微生物组.

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科学领域:

  • 微生物学 微生物学
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 口腔微生物群包括700多种细菌,对口腔健康至关重要.
  • 非生物因素在微生物组样本分析中引入批量效应,使解释复杂化.
  • 现有的批量效应去除和分类方法往往是多步或低效的.

研究的目的:

  • 开发一种统一的,单步的方法,同时减轻批量效应和分类口腔微生物群疾病.
  • 评估LassoNet在处理批量效应和提高分类准确性的批量损失方面的有效性.
  • 确定与疾病结果相关的关键口腔微生物组特征.

主要方法:

  • 实施一种新型的一步计算模型,集成批量效应去除和疾病分类.
  • 采用了LassoNet架构,具有特定的批量损失功能,用于同时处理.
  • 在五个口腔微生物组数据集中验证了模型,将性能与基线模型进行比较.

主要成果:

  • 建议的一步方法在五项研究中实现了0.8的曲线下的平均面积.
  • 与现有的基线模型相比,在口腔微生物组分析中表现出优异的性能.
  • 通过特征重要性分析,成功确定了与疾病状况相关的关键口腔微生物组特征.

结论:

  • 一步式的LassoNet方法有效地解决了批量效应,并同时分类口腔微生物组相关疾病.
  • 这种方法为传统的两步程序提供了更有效,更准确的替代方案.
  • 特性重要性分析为口腔疾病的微生物生物标志物提供了宝贵的见解.