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负数据集选择影响多种细菌物种促进者的基于机器学习的预测指标.

Marcelo González1, Roberto E Durán2,3, Michael Seeger2,3

  • 1Departamento de Electrónica, Universidad Técnica Federico Santa María, Avenida España 1680, Valparaíso 2390123, Chile.

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概括

用于细菌促进体预测的机器学习模型可能会受到负数据集选择的偏差. 使用GC平衡的数据集,如合成随机序列,可以提高跨细菌物种的模型概括性.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 基因组学就是基因组学.
  • 机器学习 机器学习

背景情况:

  • 机器学习的进步改善了细菌促进体的预测.
  • 现有的模型可能会忽略负面数据集影响,导致GC内容偏差.
  • 这种偏见在多种促进者分类中尤为重要.

研究的目的:

  • 调查由于负数据集选择而导致的多种促进者分类模型中的偏差.
  • 探索合成随机序列 (SRS) 以减轻GC内容偏差.
  • 提高细菌促进体预测模型的通用性.

主要方法:

  • 评估了使用编码序列 (CDS) 作为负数据集的多种促进子预测器.
  • 评估偏差使用特异性和敏感度指标以及缩小维度.
  • 使用标准负数据集与GC平衡的SRS数据集进行性能比较.
  • 使用DNABERT进行促销商分类.

主要成果:

  • 多种预测器在使用CDS作为负数据时显示了GC内容偏差.
  • 在真实基因组数据中,SRS数据集减少了偏差和背景噪声检测.
  • 在这两种情景中,DNABERT表现出了卓越的性能.
  • 在细菌中,GC平衡的数据集增强了促进者预测器的概括性.

结论:

  • 负数据集中的GC含量偏差影响多种细菌促进体预测.
  • 包括SRS在内的GC平衡数据集可以提高模型性能和通用性.
  • DNABERT显示,在细菌物种中进行强大的促销者预测具有前景.