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An ensemble of reduced alphabets with protein encoding based on grouped weight for predicting DNA-binding proteins

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Abstract

It is well known in the literature that an ensemble of classifiers obtains good performance with respect to that obtained by a stand-alone method. Hence, it is very important to develop ensemble methods well suited for bioinformatics data. In this work, we propose to combine the feature extraction method based on grouped weight with a set of amino-acid alphabets obtained by a Genetic Algorithm. The proposed method is applied for predicting DNA-binding proteins. As classifiers, the linear support vector machine and the radial basis function support vector machine are tested. As performance indicators, the accuracy and Matthews’s correlation coefficient are reported. Matthews’s correlation coefficient obtained by our ensemble method is ≈0.97 when the jackknife cross-validation is used. This result outperforms the performance obtained in the literature using the same dataset where the features are extracted directly from the amino-acid sequence.

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Notes

  1. 107 DNA-binding proteins and 196 non-DNA-binding proteins have a hit in the GO database.

  2. Implemented as in the OSU svm matlab toolbox

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Acknowledgments

The author would like to thank Y. Fang for sharing the dataset.

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Authors and Affiliations

  1. DEIS, IEIIT––CNR, Università di Bologna, Viale Risorgimento 2, 40136, Bologna, Italy

    Loris Nanni & Alessandra Lumini

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  1. Loris Nanni
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  2. Alessandra Lumini
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Corresponding author

Correspondence to Loris Nanni.

Appendix: Matlab code

Appendix: Matlab code

The following function implements the base feature extraction method as detailed in “Materials and methods”:

figure a

The following rows implements the main of the system:

figure b

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Nanni, L., Lumini, A. An ensemble of reduced alphabets with protein encoding based on grouped weight for predicting DNA-binding proteins. Amino Acids 36, 167–175 (2009). https://doi.org/10.1007/s00726-008-0044-7

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