title: Meta-learning framework applied in bioinformatics inference system design.
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.
authors.forename: Tomás
authors.initials: T
authors.lastname: Arredondo
authors.forename: Wladimir
authors.initials: W
authors.lastname: Ormazábal
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001419:Bacteria / Q000378:metabolism*; D001426:Bacterial Proteins / Q000737:chemistry* / Q000378:metabolism*; D003198:Computer Simulation; D015201:Meta-Analysis as Topic; D055442:Metabolome / Q000502:physiology*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D001426:Bacterial Proteins