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

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.