Abstract
Theoretical and computational methods for the prediction of protein subcellular localization have been proposed and are developing continuously. Many representations of protein sequence are proposed but a new problem arises: how to organize them together to improve prediction. It is an available solution to serialize multiple representations to single bigger one, but is still hard to avoid calculation error derived from greatly different feature values and causes huge computational burden natively because of high dimensional feature vector. We present a novel method based on decision templates(DT) for such problems in this paper. First, a protein sequence is represented as three new types of feature vectors. Then, the feature vectors are further taken as the inputs of individual SVM classifiers respectively. Finally, the outputs of these classifiers are aggregated by decision templates. The results demonstrate that DT is superior to other methods of subcellular localization prediction.
Chapter PDF
Similar content being viewed by others
Keywords
References
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)
Reinhardt, A., Hubbard, T.: Using Neural Networks for Prediction of the Subcellular Localization of Proteins. Nucleic Acids Research 26, 2230–2236 (1998)
Chou, K.C., Elrod, D.: Protein Subcellular Localization Prediction. Protein Eng. 12, 107–118 (1999)
Hua, S.J., Sun, Z.R.: Support Vector Machine Approach for Protein Subcellular Localization Prediction. Bioinformatics 17, 721–728 (2001)
Chou, K.C.: Prediction of Protein Cellular Attributes Using Pseudo-Amino Acid Composition. Proteins: Struct. Funct. Genet. 43, 246–255 (2001)
Pan, Y.X., Zhang, Z.Z., Guo, Z.M., Feng, G.Y., Huang, Z., He, L.: Application of Pseudo Amino Acid Composition for Predicting Protein Subcellular Location: Stochastic Signal Processing Approach. Journal of Protein Chemistry 22, 395–402 (2003)
Gao, Y., Shao, S.H., Xiao, X., Ding, Y.S., Huang, Y.S., Huang, Z.D., Chou, K.C.: Using Pseudo Amino Acid Composition to Predict Protein Subcellular Location: Approached with Lyapunov Index, Bessel Function, and Chebyshev Filter. Amino Acids 28, 373–376 (2005)
Shi, J.Y., Zhang, S.W., Pan, Q., Cheng, Y.M., Xie, J.: Prediction of Protein Subcellular Localization by Support Vector Machines Using Multi-Scale Energy and Pseudo Amino Acid Composition. Amino Acids 33, 69–74 (2007)
Park, K.J., Kanehisa, M.: Prediction of Protein Subcellular Locations by Support Vector Machines Using Compositions of Amino Acids and Amino Acid Pairs. Bioinformatics 19, 1656–1663 (2003)
Cui, Q., Jiang, T., Liu, B., Ma, S.: Esub8: A Novel Tool to Predict Protein Subcellular Localizations in Eukaryotic Organisms. BMC Bioinformatics 5, 66–72 (2004)
Bhasin, M., Raghava, G.P.S.: Eslpred: SVM-Based Method for Subcellular Localization of Eukaryotic Proteins Using Dipeptide Composition and Psi-Blast. Nucl. Acids Res. 32, W414–W419 (2004)
Shi, J.Y., Zhang, S.W., Liang, Y., Pan, Q.: Prediction of Protein Subcellular Localizations Using Moment Descriptors and Support Vector Machine. In: Rajapakse, J.C., Wong, L., Acharya, R. (eds.) PRIB 2006. LNCS (LNBI), vol. 4146, pp. 105–114. Springer, Heidelberg (2006)
Shi, J.Y., Zhang, S.W., Pan, Q., Zhou, G.-P.: Amino Acid Composition Distribution: A Novel Sequence Representation for Prediction of Protein Subcellular Localization. In: The 1st IEEE International Conference on Bioinformatics and Biomedical Engineering, pp. 115–118. IEEE Computer Society Press, Los Alamitos (2007)
Xiao, X., Shao, S.H., Ding, Y.S., Huang, Z.D., Huang, Y., Chou, K.C.: Using Complexity Measure Factor to Predict Protein Subcellular Location. Amino Acids 28, 57–61 (2005)
Höglund, A., Dönnes, P., Blum, T., Adolph, H.-W., Kohlbacher, O.: Multiloc: Prediction of Protein Subcellular Localization Using N-Terminal Targeting Sequences, Sequence Motifs and Amino Acid Composition. Bioinformatics 22, 1158–1165 (2006)
Mallat, S.: A Wavelet Tour of Signal Processing, 2nd edn. Academic Press, London (1999)
Kawashima, S., Ogata, H., Kanehisa, M.: AAindex: Amino Acid Index Database. Nucleic Acids Research 27, 368–369 (1999)
Huang, Y., Li, Y.D.: Prediction of Protein Subcellular Locations Using Fuzzy K-NN Method. Bioinformatics 20, 21–28 (2004)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Kreßel, U.H.: Pairwise Classification and Support Vector Machines. In: Schölkopf, B., Burges, C.J., Smola, A.J. (eds.) Advances in Kernel Methods: Support Vector Learning, pp. 255–268. MIT Press, Cambridge, MA (1999)
Platt, J., Cristianini, N., Shawe-Taylor, J.: Large Margin Dags for Multiclass Classification. Advances in Neural Information Processing Systems 12, 547–553 (2000)
Hsu, C., Lin, C.J.: A Comparison of Methods for Multi-Class Support Vector Machines. IEEE Transactions on Neural Networks 13, 415–425 (2002)
Rifin, R., Klautau, A.: In Defense of One-Vs-All Classification. Journal of Machine Learning Research 5, 101–141 (2004)
Kittler, J., Hatef, M., Duin, R., Matas, J.: On Combining Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 226–239 (1998)
Jain, A.K., Duin, R.P.W., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 4–37 (2000)
Kuncheva, L.I.: Switching between Selection and Fusion in Combining Classifiers: An Experiment. IEEE Transactions on Systems, Man, and Cybernetics, Part B 32, 146–156 (2002)
Kuncheva, L.I., Bezdek, J.C., Duin, R.: Decision Templates for Multiple Classifier Fusion: An Experimental Comparison. Pattern Recognition 34, 299–314 (2001)
Nakai, K., Horton, P.: Psort: A Program for Detecting the Sorting Signals of Proteins and Predicting Their Subcellular Localization. Trends Biochem. Sci. 24, 34–36 (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shi, J., Zhang, S., Pan, Q., Zhang, Y. (2007). Using Decision Templates to Predict Subcellular Localization of Protein. In: Rajapakse, J.C., Schmidt, B., Volkert, G. (eds) Pattern Recognition in Bioinformatics. PRIB 2007. Lecture Notes in Computer Science(), vol 4774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75286-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-75286-8_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75285-1
Online ISBN: 978-3-540-75286-8
eBook Packages: Computer ScienceComputer Science (R0)