Meta-learning framework applied in bioinformatics inference system design
| Autor: | Tomás Arredondo, Wladimir Ormazábal |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Meta learning (computer science)
Inference system education Inference Library and Information Sciences Biology Machine learning computer.software_genre Bioinformatics Models Biological General Biochemistry Genetics and Molecular Biology Pattern Recognition Automated Bacterial Proteins Meta-Analysis as Topic Artificial Intelligence Sequence Analysis Protein Computer Simulation Adaptive neuro fuzzy inference system Artificial neural network Bacteria business.industry Bacterial degradation ComputingMethodologies_PATTERNRECOGNITION Workflow Metabolome Data mining Artificial intelligence business computer Algorithms Information Systems Signal Transduction |
| Zdroj: | International journal of data mining and bioinformatics. 11(2) |
| ISSN: | 1748-5673 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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