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Negative dataset selection impacts machine learning-based predictors for multiple bacterial species promoters.

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

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

Bioinformatics (Oxford, England)
|March 28, 2025
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Summary
This summary is machine-generated.

Machine learning models for bacterial promoter prediction can be biased by negative dataset selection. Using GC-balanced datasets, like synthetic random sequences, improves model generalizability across bacterial species.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Machine Learning

Background:

  • Machine learning advances have improved bacterial promoter prediction.
  • Existing models may overlook negative dataset impacts, causing GC-content bias.
  • This bias is particularly relevant in multi-species promoter classification.

Purpose of the Study:

  • Investigate bias in multi-species promoter classification models due to negative dataset selection.
  • Explore synthetic random sequences (SRS) to mitigate GC-content bias.
  • Enhance the generalizability of bacterial promoter prediction models.

Main Methods:

  • Evaluated multi-species promoter predictors using coding sequences (CDS) as negative datasets.
  • Assessed bias using specificity and sensibility metrics and dimensionality reduction.
  • Compared performance using standard negative datasets versus GC-balanced SRS datasets.
  • Utilized DNABERT for promoter classification.

Main Results:

  • Multi-species predictors showed GC-content bias when using CDS as negative data.
  • The SRS dataset reduced bias and background noise detection in real genomic data.
  • DNABERT demonstrated superior performance in both scenarios.
  • GC-balanced datasets enhance promoter predictor generalizability in Bacteria.

Conclusions:

  • GC-content bias in negative datasets affects multi-species bacterial promoter prediction.
  • GC-balanced datasets, including SRS, can improve model performance and generalizability.
  • DNABERT shows promise for robust promoter prediction across bacterial species.