Abstract
Protein Fold recognition (PFR) is considered as an important step towards protein structure prediction. It also provides significant information about general functionality of a given protein. Despite all the efforts have been made, PFR still remains unsolved. It is shown that appropriately extracted features from the physicochemical-based attributes of the amino acids plays crucial role to address this problem. In this study, we explore 55 different physicochemical-based attributes using two novel feature extraction methods namely segmented distribution and segmented density. Then, by proposing an ensemble of different classifiers based on the AdaBoost.M1 and Support Vector Machine (SVM) classifiers which are diversely trained on different combinations of features extracted from these attributes, we outperform similar studies found in the literature for over 2% for the PFR task.
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Kavousi, K., Moshiri, B., Sadeghi, M., Araabi, B.N., Moosavi-Movahedi, A.A.: A protein fold classifier formed by fusing different modes of pseudo amino acid composition via pssm. Computational Biology and Chemistry 35(1), 1–9 (2011)
Dehzangi, A., Phon Amnuaisuk, S., Ng, K.H., Mohandesi, E.: Protein Fold Prediction Problem Using Ensemble of Classifiers. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009, Part II. LNCS, vol. 5864, pp. 503–511. Springer, Heidelberg (2009)
Krishnaraj, Y., Reddy, C.K.: Boosting methods for protein fold recognition: An empirical comparison. In: Proceedings of the 2008 IEEE International Conference on Bioinformatics and Biomedicine, pp. 393–396 (2008)
Dehzangi, A., Phon-Amnuaisuk, S., Dehzangi, O.: Using random forest for protein fold prediction problem: An empirical study. Journal of Information Science and Engineering 26(6), 1941–1956 (2010)
Ding, C., Dubchak, I.: Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 17, 349–358 (2001)
Kurgan, L.A., Zhang, T., Zhang, H., Shen, S., Ruan, J.: Secondary structure-based assignment of the protein structural classes. Amino Acids 35, 551–564 (2008)
Gromiha, M.M., Oobatake, M., Sarai, A.: Important amino acid properties for enhanced thermostability from mesophilic to thermophilic proteins. Biophysical Chemistry 82, 51–67 (1999)
Dehzangi, A., Phon-Amnuaisuk, S.: Fold prediction problem: The application of new physical and physicochemical- based features. Protein and Peptide Letters 18(2), 174–185 (2011)
Chen, K., Kurgan, L.A.: Pfres: protein fold classification by using evolutionary information and predicted secondary structure. Bioinformatics 23(21), 2843–2850 (2007)
Shen, H.B., Chou, K.C.: Predicting protein fold pattern with functional domain and sequential evolution information. Journal of Theoretical Biology 256(3), 441–446 (2009)
Yang, J.Y., Chen, X.: Improving taxonomy-based protein fold recognition by using global and local features. Protein 79(7), 2053–2064 (2011)
Shen, H.B., Chou, K.C.: Ensemble classifier for protein fold pattern recognition. Bioinformatics 22, 1717–1722 (2006)
Taguchi, Y.H., Gromiha, M.M.: Application of amino acid occurrence for discriminating different folding types of globular proteins. BMC Bioinformatics 8(1), 404 (2007)
Mathura, V.S., Kolippakkam, D.: Apdbase: Amino acid physico-chemical properties database. Bioinformation 12(1), 2–4 (2005)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: International Conference on Machine Learning, pp. 148–156 (1996)
Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)
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Dehzangi, A., Sattar, A. (2013). Protein Fold Recognition Using Segmentation-Based Feature Extraction Model. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36546-1_36
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DOI: https://doi.org/10.1007/978-3-642-36546-1_36
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