关于适当审查监督学习中使用的数据集,预测代谢途径参与的警告故事
Erik D Huckvale1, Hunter N B Moseley1,2,3,4
1Markey Cancer Center, University of Kentucky, Lexington, Kentucky, United States of America.
PloS one
|May 2, 2024
概括
在KEGG-SMILES数据集中的重复条目增加了机器学习模型的性能. 这项研究评估了错误的数据集,并强调了在代谢途径映射中对数据进行审查的必要性.
科学领域:
- 生物化学 生物化学
- 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:
- 对细胞代谢途径的代谢物特定数据映射对于生物化学解释至关重要.
- 机器学习,特别是深度学习,用于使用已知的数据集进行代谢物到路径映射预测.
- 基因和基因组的京都百科全书 (KEGG) 是此类培训数据集的来源.
研究的目的:
- 描述和评估错误的KEGG-SMILES数据集.
- 为了识别使用这种有缺陷的数据集的出版物.
- 为了证明重复输入对机器学习模型性能的影响.
主要方法:
- 对KEGG-SMILES数据集对重复条目进行分析.
- 识别使用KEGG-SMILES数据集的先前研究.
- 在删除数据集重复之前和之后对机器学习模型性能的评估.
主要成果:
- 在KEGG-SMILES数据集中,复制条目占很大一部分 (~26%).
- 重复的存在大大增加了机器学习模型的k倍交叉验证性能.
- 删除数据集的重复导致报告模型性能减少.
结论:
- KEGG-SMILES数据集是错误的,不应该用于训练机器学习模型.
- 基于此数据集的先前机器学习结果需要进行批判性重新评估.
- 对基准数据集进行适当的审查对于避免在机器学习研究中夸大性能指标至关重要.
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