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Wavelet images and Chou’s pseudo amino acid composition for protein classification

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Abstract

The last decade has seen an explosion in the collection of protein data. To actualize the potential offered by this wealth of data, it is important to develop machine systems capable of classifying and extracting features from proteins. Reliable machine systems for protein classification offer many benefits, including the promise of finding novel drugs and vaccines. In developing our system, we analyze and compare several feature extraction methods used in protein classification that are based on the calculation of texture descriptors starting from a wavelet representation of the protein. We then feed these texture-based representations of the protein into an Adaboost ensemble of neural network or a support vector machine classifier. In addition, we perform experiments that combine our feature extraction methods with a standard method that is based on the Chou’s pseudo amino acid composition. Using several datasets, we show that our best approach outperforms standard methods. The Matlab code of the proposed protein descriptors is available at http://bias.csr.unibo.it/nanni/wave.rar.

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Notes

  1. Available at http://www.genome.jp/dbget/aaindex.html. We have not considered the properties where the amino acids have value 0 or 1.

  2. http://sourceforge.net/projects/svm/.

  3. The IDs of the properties are available at http://bias.csr.unibo.it\nanni\IDw.docx.

  4. http://www.cse.oulu.fi/Downloads/LPQMatlab.

  5. http://www.cse.oulu.fi/MVG/Downloads/LBPMatlab.

  6. Implemented as in DDtool 0.95 Matlab Toolbox.

  7. It is performed 10 times and the average results are reported.

  8. For a multi-class classification with a two-class classifiers the one-versus-one or one-versus-all approach should be used (Cristianini 2000).

  9. Before the fusion the scores of each method are normalized to mean 0 and standard deviation 1.

  10. We have tested both linear and Gaussian kernels, the parameters are estimated using a grid search in the training set.

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Acknowledgments

We wish to thank Ojansivu and Heikkila for sharing their LPQ code; Rahtu, Salo and Heikkila for sharing their MSAhist code; Ahonen, Matas, He and Pietikäinen for sharing their LBP-HF code.

Conflict of interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Department of Information Engineering, University of Padua, Via Gradenigo, 6, 35131, Padova, Italy

    Loris Nanni

  2. Computer Information Systems, Missouri State University, 901 S. National, Springfield, MO 65804, USA

    Sheryl Brahnam

  3. Department of Electronic, Informatics and Systems (DEIS), Università di Bologna, Via Venezia 52, 47023, Cesena, Italy

    Alessandra Lumini

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  1. Loris Nanni
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  2. Sheryl Brahnam
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  3. Alessandra Lumini
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Correspondence to Loris Nanni.

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Nanni, L., Brahnam, S. & Lumini, A. Wavelet images and Chou’s pseudo amino acid composition for protein classification. Amino Acids 43, 657–665 (2012). https://doi.org/10.1007/s00726-011-1114-9

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