Abstract
Prediction of tertiary structure of protein from its primary structure (amino acid sequence of protein) without relying on sequential similarity is a challenging task for bioinformatics and biological science. The protein fold prediction problem can be expressed as a prediction problem that can be solved by machine learning techniques. In this paper, a new method based on ensemble of five classifiers (Naïve Bayes, Multi Layer Perceptron (MLP), Support Vector Machine (SVM), LogitBoost and AdaBoost.M1) is proposed for the protein fold prediction problem. The dataset used in this experiment is from the standard dataset provided by Ding and Dubchak. Experimental results show that the proposed method enhanced the prediction accuracy up to 64% on an independent test dataset, which is the highest prediction accuracy in compare with other methods proposed by the works have done by literature.
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Dehzangi, A., Phon Amnuaisuk, S., Ng, K.H., Mohandesi, E. (2009). Protein Fold Prediction Problem Using Ensemble of Classifiers. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_56
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DOI: https://doi.org/10.1007/978-3-642-10684-2_56
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