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Hierarchical classification of G-protein-coupled receptors with a PSO/ACO algorithm

By Nicholas Holden and Alex A. Freitas

Abstract

In our previous work we have proposed a hybrid Particle Swarm Optimisation / Ant Colony Optimisation (PSO/ACO) algorithm for discovering classification rules. In this paper we propose some modifications to the algorithm and apply it to a challenging hierarchical classification problem. This is a bioinformatics problem involving the prediction of G-Protein-Coupled Receptor’s (GPCR) hierarchical functional classes. We report the results of an extensive comparison between four versions of swarm intelligence algorithms – two versions based on our proposed algorithm and two versions based on Discrete PSO for discovering classification rules proposed in the literature. The experiments also compared the effectiveness of different kinds of protein signatures when used as predictor attributes, namely Prints, Interpro and Prosite signatures. I

Year: 2006
OAI identifier: oai:CiteSeerX.psu:10.1.1.182.9945
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