Multi-label Hierarchical Classification of Protein Functions with Artificial Immune Systems

Authors:
Roberto T. Alves;Myriam R. Delgado;Alex A. Freitas
Affiliations:
Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial, UFTPR, Curitiba, Brazil CEP: 80230-901;Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial, UFTPR, Curitiba, Brazil CEP: 80230-901;Computing Laboratory and Centre for BioMedical Informatics, University of Kent, Canterbury, U.K. CT2 7NF
Venue:
BSB '08 Proceedings of the 3rd Brazilian symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
Year:
2008

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Abstract

This work proposes two versions of an Artificial Immune System (AIS) - a relatively recent computational intelligence paradigm --- for predicting protein functions described in the Gene Ontology (GO). The GO has functional classes (GO terms) specified in the form of a directed acyclic graph, which leads to a very challenging multi-label hierarchical classification problem where a protein can be assigned multiple classes (functions, GO terms) across several levels of the GO's term hierarchy. Hence, the proposed approach, called MHC-AIS (Multi-label Hierarchical Classification with an Artificial Immune System), is a sophisticated classification algorithm tailored to both multi-label and hierarchical classification. The first version of the MHC-AIS builds a global classifier to predict all classes in the application domain, whilst the second version builds a local classifier to predict each class. In both versions of the MHC-AIS the classifier is expressed as a set of IF-THEN classification rules, which have the advantage of representing comprehensible knowledge to biologist users. The two MHC-AIS versions are evaluated on a dataset of DNA-binding and ATPase proteins.