微生物组研究中的机器学习方法:挑战和最佳实践
Georgios Papoutsoglou1,2, Sonia Tarazona3, Marta B Lopes4,5
1Department of Computer Science, University of Crete, Heraklion, Greece.
Frontiers in microbiology
|October 9, 2023
概括
微生物组数据分析的机器学习为结直肠癌提供了洞察力. 多变量特征选择和随机森林建模提高了诊断准确性,而后勤回归提供了生物学理解.
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
- 生物信息学是一种生物信息学.
背景情况:
- 使用机器学习 (ML) 的微生物组数据分析涉及数据预处理,特征选择和模型解释的复杂挑战.
- 翻译应用需要强大的方法来诊断疾病和从复杂的生物数据集中发现生物标志物.
研究的目的:
- 为微生物组数据分析中的ML工作流提供建议.
- 评估不同的ML方法用于结肠直肠癌诊断和生物标志物发现,使用枪元基因组学数据.
主要方法:
- 预处理技术的比较,包括组合转换和过.
- 评估多变量特征选择算法 (例如,统计学等价签名).
- 应用随机森林和物流回归模型与个人有条件预期 (ICE) 图片进行解释.
主要成果:
- 组合转换和过并没有始终提高预测性能.
- 多变量特征选择,特别是统计等价签名算法,有效地减少了分类错误.
- 随机森林建模与统计等价签名相结合,在测试数据集上实现了最准确的性能估计.
结论:
- 特定特征选择方法对于准确的基于微生物群的疾病预测至关重要.
- 可解释的模型,如物流回归与ICE图表,可以为临床医生和研究人员提供有价值的生物学见解.
- 该研究为开发有效的ML管道用于微生物组数据分析提供了实际指导.
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