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Towards an Ensemble Learning Strategy for Metagenomic Gene Prediction

  • Fabiana Goés
  • Ronnie Alves
  • Leandro Corrêa
  • Cristian Chaparro
  • Lucinéia Thom
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8826)

Abstract

Metagenomics is an emerging field in which the power of genome analysis is applied to entire communities of microbes. A large variety of classifiers has been developed for gene prediction though there is lack of an empirical evaluation regarding the core machine learning techniques implemented in these tools. In this work we present an empirical performance evaluation of classification strategies for metagenomic gene prediction. This comparison takes into account distinct supervised learning strategies: one lazy learner, two eager-learners and one ensemble learner. Though the performance of the four base classifiers was good, the ensemble-based strategy with Random Forest has achieved the overall best result.

Keywords

Machine learning classification methods gene prediction metagenomics 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Fabiana Goés
    • 1
  • Ronnie Alves
    • 1
    • 2
    • 4
  • Leandro Corrêa
    • 1
  • Cristian Chaparro
    • 1
  • Lucinéia Thom
    • 3
  1. 1.PPGCCUniversidade Federal do ParáBelémBrazil
  2. 2.Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier, UMR 5506Université Montpellier 2, Centre National de la Recherche ScientifiqueMontpellierFrance
  3. 3.PPGCUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
  4. 4.Institut de Biologie ComputationnelleMontpellierFrance

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