A Hierarchical Classification Ant Colony Algorithm for Predicting Gene Ontology Terms

Authors:
Fernando E. Otero;Alex A. Freitas;Colin G. Johnson
Affiliations:
Computing Laboratory, University of Kent, Canterbury, UK;Computing Laboratory, University of Kent, Canterbury, UK;Computing Laboratory, University of Kent, Canterbury, UK
Venue:
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Year:
2009

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Abstract

This paper proposes a novel Ant Colony Optimisation algorithm for the hierarchical problem of predicting protein functions using the Gene Ontology (GO). The GO structure represents a challenging case of hierarchical classification, since its terms are organised in a direct acyclic graph fashion where a term can have more than one parent -- in contrast to only one parent in tree structures. The proposed method discovers an ordered list of classification rules which is able to predict all GO terms independently of their level. We have compared the proposed method against a baseline method, which consists of training classifiers for each GO terms individually, in five different ion-channel data sets and the results obtained are promising.