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Subtree selection in kernels for graph classification.

Mehmet Tan1, Faruk Polat2, Reda Alhajj3

  • 1Department of Computer Engineering, TOBB University of Economics and Technology, Ankara, Turkey. mtan@etu.edu.tr

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This study introduces a novel feature selection method for graph kernels, enhancing the in silico prediction of small molecule properties like toxicity and activity using subtree features.

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Area of Science:

  • Bioinformatics and Cheminformatics
  • Machine Learning
  • Computational Chemistry

Background:

  • Structured data classification is crucial in bioinformatics and cheminformatics.
  • Predicting small molecule properties (toxicity, mutagenicity, activity) is a key challenge.
  • Existing methods may not fully leverage graph structural information.

Purpose of the Study:

  • To propose a new feature selection method for graph kernels.
  • To improve the accuracy of in silico prediction of small molecule properties.
  • To utilize graph subtrees as effective feature sets.

Main Methods:

  • Developed a novel feature selection technique for graph kernels.
  • Employed a masking procedure for feature selection based on graph subtrees.
  • Compared the proposed method against frequent subgraph-based approaches.

Main Results:

  • The proposed subtree-based feature selection method demonstrated effectiveness.
  • Experimental results on multiple datasets validated the approach.
  • Performance was competitive with existing frequent subgraph methods.

Conclusions:

  • The novel feature selection method enhances graph kernel performance for molecular property prediction.
  • Subtree features offer a valuable approach for structured data classification in cheminformatics.
  • This method provides a promising tool for in silico drug discovery and development.