A comparison of classification methods for gene prediction in metagenomics

Metagenomics is an emerging field in which the power of genome analysis is applied to entire communities of microbes. It is focused on the understanding of the mixture of genes (genomes) in a community as whole. The gene prediction task is a well-known problem in genomics, and it remains an interesting computational challenge in metagenomics too. A large variety of classifiers has been developed for gene prediction though there is lack of an empirical evaluation regarding the core machine learning techniques implemented in these tools. In this work we present an empirical performance comparison of different classification strategies for gene prediction in metagenomic data. This comparison takes into account distinct supervised learning strategies: one lazy learner, two eager-learners and one ensemble learner. The ensemble-based strategy has achieved the overall best result and it is competitive with the prediction baselines of well-known metagenomics tools.