Title: Meta-learning framework applied in bioinformatics inference system design

Authors: Tomás Arredondo; Wladimir Ormazábal

Addresses: Departamento de Electrónica, Universidad Técnica Federico Santa María, Valparaiso, Chile ' Departamento de Electrónica, Universidad Técnica Federico Santa María, Valparaiso, Chile

Abstract: This paper describes a meta-learner inference system development framework which is applied and tested in the implementation of bioinformatic inference systems. These inference systems are used for the systematic classification of the best candidates for inclusion in bacterial metabolic pathway maps. This meta-learner-based approach utilises a workflow where the user provides feedback with final classification decisions which are stored in conjunction with analysed genetic sequences for periodic inference system training. The inference systems were trained and tested with three different data sets related to the bacterial degradation of aromatic compounds. The analysis of the meta-learner-based framework involved contrasting several different optimisation methods with various different parameters. The obtained inference systems were also contrasted with other standard classification methods with accurate prediction capabilities observed.

Keywords: bioinformatics; data mining; meta-learning; inference systems; neural networks; machine learning; inference system design; bacterial metabolic pathway maps; genetic sequences; bacterial degradation; aromatic compounds.

DOI: 10.1504/IJDMB.2015.066775

International Journal of Data Mining and Bioinformatics, 2015 Vol.11 No.2, pp.139 - 166

Accepted: 03 Jun 2013
Published online: 05 Jan 2015 *

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