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Updated: Jan 18, 2026

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Microbe Drug Association Prediction with Bernoulli Random Forests.

Jia Qu1, Qing-Nuo Li1, Zi-Hao Song1

  • 1The School of Computer Science and Artificial Intelligence & Aliyun School of Big Data, Changzhou University, Changzhou, China.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|September 11, 2025
PubMed
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This study introduces a new computational model, Bernoulli random forest for microbe-drug association (BRFMDA) prediction, to identify potential antibiotic treatments against drug-resistant microbes. The model demonstrates high accuracy in predicting microbe-drug associations, aiding new drug development.

Area of Science:

  • Computational biology
  • Drug discovery
  • Microbiology

Background:

  • Rising antibiotic resistance in microbes necessitates novel therapeutic strategies.
  • Understanding microbe-drug associations is crucial for developing effective antibiotics.
  • Existing methods for predicting microbe-drug associations require enhancement.

Purpose of the Study:

  • To develop and validate a novel computational model for predicting microbe-drug associations.
  • To leverage integrated microbe and drug similarity features for enhanced prediction accuracy.
  • To provide a reliable tool for identifying potential drug candidates against resistant microbes.

Main Methods:

  • Development of a Bernoulli random forest (BRF) model for microbe-drug association prediction (BRFMDA).
Keywords:
Bernoulli random forestsassociations predictiondrugfilter-based approachmicrobe

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  • Integration of microbe and drug similarity metrics to construct feature representations.
  • Application of filter-based feature selection and rigorous cross-validation (LOOCV, 5-fold) for performance evaluation.

    • Main Results:

    • BRFMDA achieved high predictive performance on both the Microbe-Drug Association Database (MDAD) and an abiofilm dataset.
    • Global LOOCV yielded an Area Under the Curve (AUC) of 0.9134 on MDAD and 0.9130 on the abiofilm dataset.
    • Cross-validation results confirm BRFMDA as a dependable model for predicting potential microbe-drug associations.

    Conclusions:

    • The developed BRFMDA model offers a robust and accurate approach for predicting microbe-drug associations.
    • This computational tool can significantly accelerate the discovery of new antibiotics to combat antimicrobial resistance.
    • BRFMDA provides valuable insights for researchers in drug development and antimicrobial stewardship.