Optimization of Multi-classifiers for Computational Biology: Application to the Gene Finding Problem

Genomes of many organisms have been sequenced over the last few years. However, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction programs have been developed to address part of this problem: the location of genes along a genome. We propose a multiobjective methodology to combine algorithms into an aggregation scheme in order to obtain optimal methods' aggregations. Results show a major improvement in specificity and sensitivity when our methodology is compared to the performance of individual methods for gene finding problems. The here proposed methodology is an automatic method generator, and a step forward to exploit all already existing methods, by providing optimal methods' aggregations to answer concrete queries for a certain biological problem with a maximized accuracy of the prediction. As more approaches are integrated for each of the presented problems, de novo accuracy can be expected to improve further.