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A hierarchical classification ant colony algorithm for predicting gene ontology terms

Otero, Fernando E.B. and Freitas, Alex A. and Johnson, Colin G. (2009) A hierarchical classification ant colony algorithm for predicting gene ontology terms. In: Pizzuti, C. and Ritchie, M.D. and Giacobini, M., eds. Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics 7th European Conference. Lecture Notes in Computer Science, Lectur . Springer, Berlin, Germany, pp. 68-79. ISBN 978-3-642-01183-2. E-ISBN 978-3-642-01184-9. (doi:10.1007/978-3-642-01184-9_7) (KAR id:24128)


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.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-642-01184-9_7
Uncontrolled keywords: ant colony optimization, data mining, classification, bioinformatics
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Fernando Otero
Date Deposited: 29 Mar 2010 12:16 UTC
Last Modified: 16 Nov 2021 10:02 UTC
Resource URI: (The current URI for this page, for reference purposes)

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