Optimization of multi-classifiers for computational biology: application to gene finding and expression Romero Zaliz, Rocio Celeste Zwir Nawrocki, Jorge Sergio Igor Val Muñoz, María Coral Del Multiobjective Gene finding Gene expression Inteligencia artificial Artificial intelligence 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 and their expression. We propose a multi-objective methodology to combine state-of-the-art algorithms into an aggregation scheme in order to obtain optimal methods’ aggregations. The results obtained show a major improvement in sensitivity when our methodology is compared to the performance of individual methods for gene finding and gene expression problems. The methodology proposed here is an automatic method generator, and a step forward to exploit all already existing methods, by providing alternative 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. 2022-11-11T10:01:42Z 2022-11-11T10:01:42Z 2009-10-15 info:eu-repo/semantics/article Romero-Zaliz, R... [et al.]. Optimization of multi-classifiers for computational biology: application to gene finding and expression. Theor Chem Acc 125, 599–611 (2010). [https://doi.org/10.1007/s00214-009-0648-3] https://hdl.handle.net/10481/77919 10.1007/s00214-009-0648-3 eng http://creativecommons.org/licenses/by-nc/4.0/ info:eu-repo/semantics/openAccess Atribución-NoComercial 4.0 Internacional Springer