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Using Rotation Forest for Protein Fold Prediction Problem: An Empirical Study

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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6023))

Abstract

Recent advancement in the pattern recognition field has driven many classification algorithms being implemented to tackle protein fold prediction problem. In this paper, a newly introduced method called Rotation Forest for building ensemble of classifiers based on bootstrap sampling and feature extraction is implemented and applied to challenge this problem. The Rotation Forest is a straight forward extension of bagging algorithms which aims to promote diversity within the ensemble through feature extraction by using Principle Component Analysis (PCA). We compare the performance of the employed method with other Meta classifiers that are based on boosting and bagging algorithms, such as: AdaBoost.M1, LogitBoost, Bagging and Random Forest. Experimental results show that the Rotation Forest enhanced the protein folding prediction accuracy better than the other applied Meta classifiers, as well as the previous works found in the literature.

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Authors and Affiliations

  1. Center of Artificial Intelligence and Intelligent computing, Faculty of Information Technology, Multi Media University, Cyberjaya, Selangor, Malaysia

    Abdollah Dehzangi, Somnuk Phon-Amnuaisuk, Mahmoud Manafi & Soodabeh Safa

Authors
  1. Abdollah Dehzangi
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  2. Somnuk Phon-Amnuaisuk
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  3. Mahmoud Manafi
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  4. Soodabeh Safa
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Editor information

Editors and Affiliations

  1. Institute for High-Performance Computing and Networking (ICAR), Italian National Research Council (CNR), Via P. Bucci 41C, 87036, Rende, (CS), Italy

    Clara Pizzuti

  2. Department of Molecular Physiology and Biophysics, Vanderbilt University, Center for Human Genetics Research, 519 Light Hall, 37232, Nashville, TN, USA

    Marylyn D. Ritchie

  3. Department of Animal Production Epidemiology and Ecology, University of Torino, Molecular Biotechnology Center, Via Leonardo da Vinci 44, 10095, Grugliasco, (TO), Italy

    Mario Giacobini

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Dehzangi, A., Phon-Amnuaisuk, S., Manafi, M., Safa, S. (2010). Using Rotation Forest for Protein Fold Prediction Problem: An Empirical Study. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2010. Lecture Notes in Computer Science, vol 6023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12211-8_19

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  • DOI: https://doi.org/10.1007/978-3-642-12211-8_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12210-1

  • Online ISBN: 978-3-642-12211-8

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