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An Enhanced Computational Approach Using Multi-kernel Positive Unlabeled Learning for Predicting Drug-target

Mohammad Reza Keyvanpour1, Soheila Mehrmolaei2, Faraneh Haddadi1,2

  • 1Department of Computer Engineering, Alzahra University, Tehran, Iran.

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|April 30, 2024
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Summary
This summary is machine-generated.

This study introduces MKPUL-BLM, a hybrid approach for drug-target interactions prediction (DTIP). It enhances prediction accuracy by integrating multi-kernel and positive unlabeled learning methods, addressing challenges in existing computational approaches.

Keywords:
AUPR.Drug-target interactionsROCAUCmulti-kernelpositive-unlabeled learningpredicting targets

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Drug discovery

Background:

  • Analyzing biological networks to predict future links is crucial for drug discovery.
  • Drug-target interactions prediction (DTIP) is fundamental for identifying potential drug-target relationships.
  • Existing computational methods for DTIP face challenges like lack of negative samples and low accuracy.

Purpose of the Study:

  • To propose an efficient and hybrid approach, MKPUL-BLM, to address challenges in DTIP.
  • To improve the accuracy and reliability of predicting drug-target interactions.
  • To manage the lack of confirmed negative samples in computational drug discovery.

Main Methods:

  • The MKPUL-BLM approach combines multi-kernel and positive unlabeled learning (PUL).
  • It leverages network information to minimize small similarities and increase accuracy.
  • Potential negative samples are generated using PUL, and labels are expanded via semi-supervised learning.

Main Results:

  • The method achieved ROCAUC of 0.98 and AUPR of 0.94 on an old interactions set.
  • It improved ROCAUC to 0.89 and AUPR to 0.77 on a new interactions set.
  • These results demonstrate significant enhancements in prediction performance.

Conclusions:

  • MKPUL-BLM offers an efficient and reliable alternative for drug-target interactions prediction.
  • The hybrid approach effectively addresses limitations of previous computational methods.
  • This method contributes to advancing drug discovery through improved DTIP.