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Identification of human drug targets using machine-learning algorithms.

Priyanka Kumari1, Abhigyan Nath1, Radha Chaube2

  • 1Bioinformatics Section, Mahila Mahavidyalaya, Banaras Hindu University, Varanasi 221005, India.

Computers in Biology and Medicine
|December 2, 2014
PubMed
Summary

This study introduces a computational method to identify human drug targets using protein sequences. The approach effectively distinguishes drug targets from non-targets, aiding drug discovery.

Keywords:
Dipeptide compositionDrug targetsEnsemble learningProperty group compositionReliefFSMOTE

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Drug Discovery

Background:

  • Identifying drug targets is critical for drug discovery.
  • Computational methods accelerate the identification of potential drug targets from genomic data.

Purpose of the Study:

  • To develop a sequence-based computational method for identifying and discriminating human drug target proteins.
  • To address class imbalance in drug target prediction using SMOTE.

Main Methods:

  • Utilized sequence-based features (amino acid composition, properties, dipeptides).
  • Employed SMOTE for data balancing and Rotation Forest with ReliefF for feature selection.
  • Validated models using tenfold stratified cross-validation and leave-one-out cross-validation.

Main Results:

  • Achieved high performance metrics: up to 88.1% sensitivity, 85.5% accuracy, and 0.712 MCC.
  • Identified optimal features that may reveal compositional patterns in drug targets.
  • Demonstrated promising results on a second independent dataset.

Conclusions:

  • The developed sequence-based method is effective for predicting human drug targets.
  • This approach can serve as a valuable complementary tool in drug discovery pipelines.
  • The identified features offer insights into the characteristics of drug target proteins.