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Predicting drug target interactions using meta-path-based semantic network analysis.

Gang Fu1, Ying Ding2,3, Abhik Seal2

  • 1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA. gang.fu@nih.gov.

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|April 14, 2016
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Summary
This summary is machine-generated.

Predicting drug target interactions (DTIs) is crucial for drug discovery. This study enhances semantic network analysis using meta-path topological features, significantly improving DTI prediction accuracy over existing methods.

Keywords:
Link predictionMachine learningMeta-path topological featureRandom forestSemantic network analysis

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Machine learning

Background:

  • Drug target interactions (DTIs) are predicted using semantic network topological features.
  • Semantic networks contain heterogeneous node and link types, requiring meta-path analysis for link prediction.

Purpose of the Study:

  • To improve drug target interaction prediction by enhancing semantic network analysis.
  • To investigate the utility of meta-path topological patterns for link prediction in heterogeneous networks.

Main Methods:

  • Constructed supervised machine learning models using meta-path topological features from an enriched semantic network (Chem2Bio2RDF).
  • Expanded the network with compound and protein similarity links from PubChem.
  • Utilized Random Forest algorithm for binary classification and feature ranking.

Main Results:

  • Enriching the semantic network with similarity links significantly improved predictive performance.
  • The Random Forest model outperformed the Semantic Link Association Prediction (SLAP) algorithm in predicting DTIs.
  • Identified important topological features contributing to link prediction via Random Forest's feature ranking.

Conclusions:

  • The proposed framework offers a powerful alternative to SLAP for DTI prediction.
  • The framework integrates diverse chemical, biological, and biomedical information into a unified semantic network.
  • It allows flexible feature space enrichment and simultaneous model construction and feature selection.