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

30 Citations (SciVal)

Abstract

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
Volume4
Issue number2
DOIs
Publication statusPublished - Mar 2010

Keywords

  • Attribute selection
  • Classifier selection
  • Feature selection
  • G-protein coupled receptor
  • GPCR
  • Hierarchical classification
  • Protein function prediction
  • Supervised learning

Fingerprint

Dive into the research topics of 'Hierarchical classification of G-Protein-Coupled Receptors with data-driven selection of attributes and classifiers'. Together they form a unique fingerprint.

Cite this