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Advances in Computational Biology pp 147–154Cite as

Gene Predictors Ensemble for Complex Metagenomes

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 232))

Abstract

Is presented an ensemble of predictors of genes that focuses on improving the performance of traditional predictors when applied to metagenomes obtained by sequencing 454 and are characterized by very short reads. The proposed ensemble is based on the use of data mining techniques, such as decision trees and k-means, complemented by structural information of the sequence provided by the fractal dimension. The assembly obtained can overcome the performance from the best ab initio predictor in a proportion of 15 to 20%.

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Author information

Authors and Affiliations

  1. Facultad de Ingeniería Electrónica y Telecomunicaciones, Departamento de Sistemas, Universidad Del Cauca, Popayán, Colombia

    Nestor Díaz

  2. Facultad de Ingeniería Electrónica y Telecomunicaciones, Universidad Del Cauca, Popayán, Colombia

    Andres Felipe Ruiz Velazco & Cristian Alberto Olaya Márquez

Authors
  1. Nestor Díaz
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  2. Andres Felipe Ruiz Velazco
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  3. Cristian Alberto Olaya Márquez
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Corresponding author

Correspondence to Nestor Díaz .

Editor information

Editors and Affiliations

  1. University of Caldas, Manizales, Colombia

    Luis F. Castillo

  2. Cenicafé - Centro Nacional de Investigaciones del Café en Colombia, Chinchiná, Colombia

    Marco Cristancho

  3. University of Caldas, Manizales, Colombia

    Gustavo Isaza

  4. BIOS - Centro Bioinformática y Biologia Computacional de Colombia, Manizales, Colombia

    Andrés Pinzón

  5. Department of Computer Science School of Science, University of Salamanca, Salamanca, Spain

    Juan Manuel Corchado Rodríguez

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© 2014 Springer International Publishing Switzerland

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Díaz, N., Velazco, A.F.R., Márquez, C.A.O. (2014). Gene Predictors Ensemble for Complex Metagenomes. In: Castillo, L., Cristancho, M., Isaza, G., Pinzón, A., Rodríguez, J. (eds) Advances in Computational Biology. Advances in Intelligent Systems and Computing, vol 232. Springer, Cham. https://doi.org/10.1007/978-3-319-01568-2_22

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  • DOI: https://doi.org/10.1007/978-3-319-01568-2_22

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01567-5

  • Online ISBN: 978-3-319-01568-2

  • eBook Packages: EngineeringEngineering (R0)

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