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ISLRWR: A network diffusion algorithm for drug-target interactions prediction.

Lu Sun1, Zhixiang Yin1, Lin Lu2

  • 1School of Mathematics, Physics and Statistics, Institute for Frontier Medical Technology, Center of Intelligent Computing and Applied Statistics, Shanghai University of Engineering Science, Shanghai, China.

Plos One
|January 30, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the ISLRWR algorithm for improved drug-target interaction prediction. The novel method enhances network analysis, significantly boosting prediction accuracy in drug discovery.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Drug discovery

Background:

  • Machine learning and computational methods accelerate drug discovery by improving efficiency and reducing costs.
  • Predicting drug-target interactions (DTIs) is crucial for identifying potential drug candidates.

Purpose of the Study:

  • To develop an advanced network-based algorithm for more accurate prediction of drug-target interactions (DTIs).
  • To enhance existing network diffusion algorithms for better feature extraction in drug discovery.

Main Methods:

  • Constructed a network model using multi-source heterogeneous network information.
  • Employed and improved network diffusion algorithms: Metropolis-Hastings Random Walk (MHRW) and Improved Metropolis-Hastings Random Walk (IMRWR).
  • Developed the Isolated Self-Loop Random Walk (ISLRWR) algorithm by correcting transfer probabilities and increasing self-loop rates for isolated nodes.

Main Results:

  • The ISLRWR algorithm demonstrated significant improvements in predicting DTIs, outperforming Random Walk with Restart (RWR) and MHRW.
  • ISLRWR achieved a 7.53% and 5.72% increase in Area Under the Receiver Operating Characteristic Curve (AUROC) compared to RWR and MHRW, respectively.
  • ISLRWR showed a 5.95% and 4.19% increase in Area Under the Precision-Recall Curve (AUPRC) over RWR and MHRW, maintaining performance even after excluding homologous proteins.

Conclusions:

  • The ISLRWR algorithm represents a substantial advancement in predicting drug-target interactions.
  • This method offers improved efficiency and accuracy for computational drug discovery.
  • The algorithm's robustness against homologous protein interference highlights its practical utility.