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注意你的流行率!

Sébastien J J Guesné1, Thierry Hanser2, Stéphane Werner2

  • 1Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, West Yorkshire, LS11 5PS, UK. sebastien.guesne@lhasalimited.org.

Journal of cheminformatics
|April 15, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了评估定量结构-活动关系 (QSAR) 模型的平衡指标,提供了一个统一的框架,以克服二进制分类中依赖于流行率的性能指标问题. 均衡的指标确保在各种数据集之间进行公平的模型比较.

关键词:
平衡的马修斯相关系数的相关系数平衡的指标是平衡的.校准的指标指标.分类模型的分类模型.不平衡的 不平衡的模型验证模型验证流行程度 流行程度流行率的转移发生在流行率上.在QSAR中使用QSAR.

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科学领域:

  • 计算化学和化学信息学
  • 机器学习模型验证的验证
  • 定量结构-活动关系 (QSAR) 研究研究.

背景情况:

  • 像准确度这样的标准性能指标在二进制分类中可能会因为不平衡的数据集而导致误导.
  • 平衡精度和马修斯相关系数 (MCC) 用于减轻不平衡,但有局限性.
  • 现有的文献缺乏理解和扩展平衡指标超出平衡准确性的正式框架.

研究的目的:

  • 在QSAR模型验证的背景下,正式定义混矩阵,灵敏度和特异性.
  • 为了证明在非恒定的流行率下比较绩效指标的局限性.
  • 将均衡指标的概念扩展到马修斯相关系数 (MCC) 和其他绩效指标.

主要方法:

  • 混矩阵,灵敏度和特异性的正式定义.
  • 使用合成数据对流行程度依赖性的分析.
  • 作为灵敏度,特异性和流行度的函数,平衡度表达式的导数.

主要成果:

  • 平衡精度相当于对50%的流行率 (平衡测试集) 校准的精度.
  • 马修斯相关系数 (MCC) 已被证明是患病率依赖的,在极端患病率下潜在的低估.
  • 为平衡指标提出了一个新的框架,包括一个平衡的MCC,通过灵敏度和特异性来表达.

结论:

  • 拟议的平衡指标为QSAR模型验证提供了强大的和统一的方法,使不同流行率的数据集能够进行可靠的比较.
  • 这项工作将平衡评估的概念扩展到平衡准确度之外,为模型性能提供了更全面的理解.
  • 衍生出的混矩阵和平衡的度量表达式促进了化学信息学中的QSAR模型的准确解释和比较.