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通过全元基因组测序数据全面评估用于诊断胃肠道疾病的机器学习方法

Sungho Lee¹, Insuk Lee^1,2

¹Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Republic of Korea.

|July 7, 2024

概括

机器学习 (ML) 模型可以使用肠道微生物群数据来诊断疾病. 这项研究利用全元基因组猎枪测序 (WMS) 数据优化了ML管道,确定了准确诊断疾病的关键特征和方法.

科学领域:

微生物组研究的研究.
机器学习应用程序机器学习应用程序
疾病诊断疾病诊断

背景情况:

肠道微生物群与宿主疾病的联系提供了诊断机会.
机器学习 (ML) 管道对于分析高维微生物组数据至关重要.
在疾病诊断中,对于全元基因组枪测序 (WMS) 数据的最佳ML方法尚未确定.

研究的目的:

综合评估使用便WMS数据诊断克罗恩病和结直肠癌的ML方法.
为ML管道确定最有效的分析模式,预处理技术和分类算法.
为建立可靠的疾病诊断ML模型提供实用指南.

主要方法:

分析了来自21项病例控制研究的2,553个便WMS样本.
评估各种ML管道组件,包括特征选择,数据预处理和分类算法.
使用持久数据来验证最佳ML管道的验证,以实现概括性.

主要成果:

肠道特异性,物种级别的分类学特征对分析最有效.
批次校正并没有持续改善模型性能.
组合数据转换显著提高了模型性能.
非线性整体分类器通常优于线性模型,但线性模型对线性可分离疾病有效.

结论:

在基于ML的微生物组诊断中,物种级别的分类学概况和特定数据转换至关重要.
在选择ML算法时,应考虑疾病的潜在微生物组分离性.
这项研究提供了经过验证的ML管道和使用WMS数据进行疾病诊断的实用指南.

关键词:

人类肠道微生物组结肠直肠癌是什么意思疾病诊断疾病诊断机器学习, 克罗恩氏病整个元基因组的猎枪测序.

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