Optimization of multi-classifiers for computational biology: Application to gene finding and expression

Rocío Romero-Zaliz, Cristina Rubio-Escudero, Igor Zwir, Coral del Val

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish
Pages (from-to)599-611
Number of pages13
JournalTheoretical Chemistry Accounts
Issue number3-6
StatePublished - Mar 2010


  • Gene expression
  • Gene finding
  • Multiobjective


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