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科学领域:

  • 微生物学 微生物学
  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 分类肺腺癌 (AC) 和状细胞癌 (SCC) 是具有挑战性的,通常需要侵入性手术.
  • 诊断的延迟可能会阻碍肺癌患者及时开始治疗.

研究的目的:

  • 开发一种机器学习模型来分类AC和SCC肺癌亚型.
  • 为了利用肺组织微生物组进行准确和非侵入性的亚型化.

主要方法:

  • 利用了来自AC和SCC患者的切除肺组织微生物组数据.
  • 采用LEfSe用于差异性种类丰富和线性差异分析 (LDA) 来增强特征.
  • 基准测试了六种监督机器学习算法,包括XGBoost,后勤回归和深度神经网络.

主要成果:

  • 确定了十个潜在的微生物标记物,区分AC和SCC亚型.
  • 极端梯度提升 (XGBoost) 显示出卓越的性能,精度为76.25%,AUROC为0.81.
  • 独立数据集验证证实了模型的稳定性,AUROC为0.71.

结论:

  • 肺组织微生物组可以有效地用于分类肺癌亚型 (AC与SCC).
  • 机器学习,特别是XGBoost,为改善肺癌诊断提供了一个有希望的途径,并可能减少与传统方法相关的延迟.