过渡性注释对分类学分类人员培训的影响
Harihara Subrahmaniam Muralidharan1,2, Noam Y Fox1, Mihai Pop1,2
1Department of Computer Science, University of Maryland, College Park, MD, United States.
Frontiers in microbiology
|January 18, 2024
概括
在计算生成的数据上训练机器学习分类器,称为过渡性注释,可以显著改变微生物社区分析中的分类标签. 即使少量这样的数据也可能扭曲分类结果,影响微生物识别的准确性.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 微生物社区分析通常使用机器学习分类器为序列分配分类学标签.
- 训练数据集最好使用经过实验验证的标签,但通常依赖于计算预测 (过渡性注释).
研究的目的:
- 调查使用计算生成的训练数据对微生物分类学机器学习分类器的影响.
- 评估如何用此类数据重新训练分类器影响分类学标签预测.
主要方法:
- 利用来自元基因组实验的16S rRNA数据来生成新的训练示例.
- 在这些生成的数据上训练了Ribosomal数据库项目的贝叶斯分类器.
- 在重新培训后,评估预测的分类学标签的变化.
主要成果:
- 证明即使是少数计算生成的训练数据点也可以显著扭曲分类器输出.
- 证明了分类空间的整个区域可以被过渡性注释扰乱.
结论:
- 强调了机器学习分类器对过渡性注释训练数据的脆弱性.
- 讨论了影响分类器弹性的因素,并提出了最佳实践,以减轻来自不准确训练数据的工件.
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