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Updated: May 10, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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Bayesian Inference for Drug Discovery by High Negative Samples and Oversampling.

Manh Hung Le1, Nam Anh Dao1, Xuan Tho Dang2

  • 1Electric Power University, Hanoi, Viet Nam.

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Summary

This study introduces high negative oversampling (HNO) to improve drug repositioning by addressing imbalanced data and noisy negative samples. The novel method enhances drug discovery model performance for faster therapeutic development.

Keywords:
Bayesian inferenceDrug-disease associationsdrug repositioningimbalanced dataover-sampleprotein associations

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Area of Science:

  • Pharmacology
  • Bioinformatics
  • Computational Biology

Background:

  • Drug repositioning accelerates drug discovery but struggles with imbalanced datasets and noisy negative samples.
  • Effective identification of negative samples is critical for robust drug repositioning models.

Purpose of the Study:

  • To introduce a novel method using high negative oversampling (HNO) to tackle data imbalance and noise in drug repositioning.
  • To enhance the performance of drug discovery models by improving negative sample selection.

Main Methods:

  • Integration of high negative oversampling (HNO) with network-based graph mining, matrix factorization, and Bayesian inference.
  • Development of strategies for constructing high-quality negative samples to mitigate data noise.

Main Results:

  • Demonstrated efficacy of the HNO approach in improving drug discovery model performance.
  • Successful management of data imbalance and refinement of negative sample selection.

Conclusions:

  • The proposed methodology offers a robust framework for enhancing drug repositioning.
  • Potential for broader applications in various biomedical domains requiring imbalanced data analysis.