从使用xMarkerFinder的跨队列数据集中识别和验证微生物生物标志物
Wenxing Gao1, Weili Lin1, Qiang Li2
1The Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China.
Nature protocols
|May 14, 2024
概括
本研究介绍了xMarkerFinder,这是一个用于在各种研究中识别可复制微生物生物标志物的计算框架. 它通过验证生物标志物和构建分类模型来增强疾病诊断和预后.
科学领域:
- 微生物组研究的研究.
- 计算生物学是一种计算生物学.
- 发现生物标志物的发现.
背景情况:
- 微生物签名显示出作为疾病生物标志物的前景.
- 研究中的差异性阻碍了标准化生物标志物研究.
- 现有的工具往往专注于生物标志物识别的单一方面.
研究的目的:
- 引入xMarkerFinder,一个用于微生物生物标记物识别和验证的计算框架.
- 为了在不同的队列和微生物息地中实现可重复的生物标志物发现.
- 建立分类模型并探索潜在的微生物引起的疾病机制.
主要方法:
- 一个四个阶段的计算框架:差异签名识别,模型构建,模型验证和生物标志物解释.
- 跨队列数据集验证可重复性.
- 复杂的特征选择以解决队列异质性.
- 通过特异性评估进行内部和外部验证.
主要成果:
- xMarkerFinder促进了可再生微生物生物标志物的识别和验证.
- 该框架支持创建可靠的分类模型.
- 它可以适应各种微生物息地和超出肠道微生物组的数据类型.
- 综合验证确保了可靠性和特异性.
结论:
- xMarkerFinder为微生物生物标志物研究提供了一种标准化和验证的方法.
- 该框架增强了微生物签名在疾病诊断和预后方面的实用性.
- 它的适应性设计和用户友好的可访问性促进了微生物组研究的广泛应用.
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