Optimization of multi-classifiers for computational biology: application to gene finding and expression
Metadatos
Afficher la notice complèteEditorial
Springer
Materia
Multiobjective Gene finding Gene expression Inteligencia artificial Artificial intelligence
Date
2009-10-15Referencia bibliográfica
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]
Patrocinador
Ministry of Science and Innovation, Spain (MICINN) Spanish Government TIN-2006-12879; Junta de Andalucia TIC-02788; Howard Hughes Medical Institute; European Commission Junta de AndaluciaRésumé
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.