Evolutionary computation, combined with support vector machines, for gene structure prediction

Gene structure prediction consists of determining which parts of a genomic sequence of the cell are coding, and constructing the whole gene from its start site to its stop codon. Gene recognition is one of the most important open problems in bioinformatics. The subtle sources of evidence and the many pitfalls of the problem make gene recognition in eukaryotes one of the most challenging tasks in this field. Gene recognition may be considered as a search problem, where many evidence sources are combined in a scoring function that must be maximized to obtain the structure of a probable gene. Using an intrinsic method, we propose a combination of evolutionary computation and support vector machines for gene structure prediction. Specifically, we use support vector machines (SVMs) to localize and score the functional sites along the genomic sequence, reducing the search space. Evolutionary computation is used to evolve a population where the individuals are correct gene structures. The flexibility of evolutionary computation can be used to account for the complexities of the problem, which are growing as our knowledge of the molecular processes of transcription and translation deepens. Our results show that with a very simple program we are able to achieve very good accuracies in the recognition of genes in human chromosome 19.