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从基于变压器的人类微生物组进行时间学年龄估计 强大的主要组件分析.

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  • 1Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.

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概括

基于变压器的强大主要组件分析 (TRPCA) 增强了微生物组分析,用于预测人类年龄和其他表型. 这种深度学习方法可以提高各种身体部位和测序方法的准确性.

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科学领域:

  • 微生物组的分析
  • 计算生物学是一种计算生物学.
  • 机器学习是机器学习.

背景情况:

  • 深度学习模型显示了分析微生物组数据以了解人类表型的前景.
  • 现有的方法可能缺乏对复杂微生物组数据集的解释性或最佳性能.

研究的目的:

  • 为微生物组分析引入基于变压器的强大的主要组件分析 (TRPCA).
  • 评估TRPCA的性能与用于年龄预测的传统机器学习模型相比.
  • 探索TRPCA在多任务学习 (MTL) 中的实用性,用于组合预测任务.

主要方法:

  • TRPCA将变压器架构与强大的主要组件分析相结合.
  • 对年龄预测进行了基准测试,使用16S rRNA基因扩增 (16S) 和来自皮肤,口腔和肠道身体部位的全基因组测序 (WGS) 数据.
  • 多任务学习用于同时进行分类和回归任务.

主要成果:

  • TRPCA显著提高了年龄预测的准确性,显示WGS皮肤 (28%) 和16S皮肤 (14%) 样本的最大平均绝对误差 (MAE) 减少.
  • TRPCA MTL 方法在出生国预测方面实现了 89% 的准确性,并从 WGS 便样本中增强了年龄预测.
  • 剩余分析揭示了受试者与测序方法和身体部位之间的预测错误之间的联系.

结论:

  • 在人类微生物组样本中,TRPCA提供了更好的年龄预测准确度.
  • 该方法保持了特征级别的解释性,有助于理解微生物组与表型的关系.
  • TRPCA显示了复杂任务的潜力,例如多站点和多模式微生物组数据分析.