Abstract
Prediction of protein subcellular localization is one of the hot research topics in bioinformatics. In this paper, several support vector machines (SVM) with a new presented coding scheme method based on N-terminal amino compositions are first trained to discriminate between proteins destined for the mitochondrion, the chloroplast, the secretory pathway, and ‘other’ localizations. Then a decision unit is used to make the final prediction based on several SVMs’ outputs. Tested on redundancy-reduced sets, the proposed method reached 89.6 % (plant) and 91.9% (non-plant) total accuracies, which, to the best of our knowledge, are the highest ever reported using the same data sets.
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References
Jensen, L.J., Gupta, R., Blom, N., et al.: Prediction of human protein function from post-translational modifications and localization features. J. Mol. Biol. 319, 1257–1265 (2002)
Chou, K.C., Elrod, D.: Protein subcellular location prediction. Protein Eng. 12, 107–118 (1999)
Reinhardt, A., Hubbard, T.: Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Res. 26, 2230–2236 (1998)
Hua, S., Sun, Z.: Support vector machine approach for protein subcellular localization prediction. Bioinformatics 17, 721–728 (2001)
Nakashima, H., Nishikawa, K.: Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. J. Mol. Biol. 238, 54–61 (1994)
Nakai, K.: Protein sorting signals and prediction of subcellular localization. Adv. Protein Chem. 54, 277–344 (2000)
Emanuelsson, O., Nielsen, H., Brunak, S., et al.: Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J. Mol. Biol. 300, 1005–1016 (2000)
Nielsen, H., Engelbrecht, J., Brunak, S., et al.: Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Eng. 10, 1–6 (1997)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Emanuelsson, O.: Predicting protein subcellular localisation from amino acid sequence information. Briefings in Bioinformatics 3, 361–376 (2002)
Westion, J., Watkins, C.: Multi-class support vector machines. In: Verleysen, M. (ed.) Proceedings of ESANN 1999, Brussels. D. Facto Press (1999)
Hsu, C.-W., Lin, C.-J.: A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks 13, 415–425 (2002)
Matthews, B.W.: Comparison of predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta 405, 442–451 (1975)
Rost, B., Sander, C.: Prediction of secondary structure at better than 70% accuracy. J. Mol. Biol. 232, 584–599 (1993)
Bhasin, M., Raghava, G.P.S.: ESLpred: SVM Based Method for Subcellular Localization of Eukaryotic Proteins using Dipeptide Composition and PSI-BLAST. Nucleic Acids Reasearch 32, W383 - W389 (2004)
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Li, Yf., Liu, J. (2005). Predicting Subcellular Localization of Proteins Using Support Vector Machine with N-Terminal Amino Composition. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_73
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DOI: https://doi.org/10.1007/11527503_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
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