评论 "使用基因组数据和机器学习来预测抗生素耐药性:一篇教程论文"
Davide Chicco1,2, Giuseppe Jurman3,4
1Dipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-Bicocca, Milan, Italy.
PLoS computational biology
|December 1, 2025
概括
这项研究批评了用于预测抗生素耐药性的机器学习课程. 它反对使用准确度和F1分数进行二进制分类,而是推马修斯相关系数 (MCC),并警告k倍交叉验证陷.
科学领域:
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
- 基因组学就是基因组学.
背景情况:
- 最近的一项研究提出了一种机器学习课程,用于使用基因组学数据预测抗生素耐药性.
- 该课程提供了对传统机器学习管道的逐步指南,可供初学者使用.
研究的目的:
- 批评上述研究中建议的绩效评估方法.
- 倡导使用马修斯相关系数 (MCC) 而不是准确性和F1分数来进行二进制分类.
- 突出K倍交叉验证在机器学习模型开发中的潜在陷.
主要方法:
- 发表的教学课程的正式评论和批评.
- 对二进制分类表现的统计指标的解释.
- 讨论机器学习中的数据分区技术.
主要成果:
- 准确度和F1分数被认为是对二进制分类任务的误导性指标.
- 马修斯相关系数 (MCC) 被提出作为一个更强大和可靠的指标.
- 确定K倍交叉验证是一种具有重大缺陷和潜在陷的方法.
结论:
- 在基因组学中评估二元分类模型时,应该优先考虑马修斯相关系数 (MCC).
- 建议谨慎使用k倍交叉验证,因为它固有的局限性.
- 准确的模型评估对于抗生素耐药性研究中的可靠预测至关重要.
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