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Heter-LP: A Heterogeneous Label Propagation Method for Drug Repositioning.

Maryam Lotfi Shahreza1, Nasser Ghadiri2, James R Green3

  • 1Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.

Methods in Molecular Biology (Clifton, N.J.)
|December 15, 2018
PubMed
Summary

This study introduces Heter-LP, a novel label propagation method for drug repositioning. Heter-LP effectively predicts drug-target, drug-disease, and disease-target interactions using heterogeneous biological networks, improving drug discovery efficiency.

Keywords:
Disease-target relationsDrug repositioningDrug-disease relationsDrug-target relationsHeterogeneous label propagationSemi-supervised learning

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Network Science

Background:

  • Drug repositioning offers a cost-effective and rapid approach to drug development.
  • Predicting relationships between drugs, targets, and diseases is crucial for identifying new therapeutic applications.
  • Biological networks are suitable models for understanding complex biological relationships.

Purpose of the Study:

  • To develop a novel semi-supervised learning method for predicting drug-target, drug-disease, and disease-target interactions.
  • To enhance drug repositioning strategies through accurate interaction prediction.
  • To analyze and model relationships within biological networks.

Main Methods:

  • Construction of a heterogeneous biological network integrating diverse data sources.
  • Development of a novel label propagation algorithm (Heter-LP) for heterogeneous networks.
  • Utilizing semi-supervised learning, specifically label propagation, to predict interactions without negative samples.

Main Results:

  • Heter-LP effectively integrates various data sources and uses both local and global features.
  • The method demonstrated superior performance in predicting interactions, achieving higher AUC and AUPR scores compared to existing methods.
  • Experimental evaluations and case studies validated the efficacy of Heter-LP.

Conclusions:

  • Heter-LP provides a powerful tool for drug repositioning by accurately predicting complex biological interactions.
  • The method's ability to integrate data and avoid negative samples makes it a valuable asset in drug discovery.
  • This approach accelerates the identification of potential drug candidates and their therapeutic targets.