Floating search methodology for combining classification models for site recognition in DNA sequences

Recognition of the functional sites of genes, such as translation initiation sites, donor and acceptor splice sites and stop codons, is a relevant part of many current problems in bioinformatics. Recognition of the functional sites of genes is also a fundamental step in gene structure predictions in the most powerful programs. The best approaches to this type of recognition use sophisticated classifiers, such as support vector machines. However, with the rapid accumulation of sequence data, methods for combining many sources of evidence are necessary as it is unlikely that a single classifier can solve this type of problem with the best possible performance. A major issue is that the number of possible models to combine is large and the use of all of these models is impractical. In this paper, we present a framework that is based on floating search for combining as many classifiers as needed for the recognition of any functional sites of a gene. The methodology can be used for the recognition of translation initiation sites, donor and acceptor splice sites and stop codons. Furthermore, we can combine any number of classifiers that are trained on any species. The method is also scalable to large datasets, as is shown in experiments in which the whole human genome is used. The method is also applicable to other recognition tasks. We present experiments on the recognition of these four functional sites in the human genome, which is used as the target genome, and use another 20 species as sources of evidence. The proposed methodology shows significant improvement over state-of-the-art methods for use in a thorough evaluation process. The proposed method is also able to improve heuristic selection of species to be used as sources of evidence as the search finds the most useful datasets.