Hierarchical classification of G-Protein-Coupled Receptors with data-driven selection of attributes and classifiers

A. Secker, M. N. Davies, A. A. Freitas, E. B. Clark, J. Timmis, D. R. Flower

Research output: Contribution to journalArticlepeer-review


We address the important bioinformatics problem of predicting protein function from a protein's primary sequence. We consider the functional classification of G-Protein-Coupled Receptors (GPCRs), whose functions are specified in a class hierarchy. We tackle this task using a novel top-down hierarchical classification system where, for each node in the class hierarchy, the predictor attributes to be used in that node and the classifier to be applied to the selected attributes are chosen in a data-driven manner. Compared with a previous hierarchical classification system selecting classifiers only, our new system significantly reduced processing time without significantly sacrificing predictive accuracy.

Original languageEnglish
Pages (from-to)191-210
Number of pages20
JournalInternational journal of data mining and bioinformatics
Issue number2
Publication statusPublished - Mar 2010


  • hierarchical classification
  • supervised learning
  • attribute selection
  • feature selection
  • classifier selection
  • protein function prediction
  • GPCR
  • G-protein coupled receptor

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