Hilbert Huang Transform for Predicting Proteins Subcellular Location

Abstract

Apoptosis proteins have a central role in the development and homeostasis of an organism. These proteins are very important for the understanding the mechanism of programmed cell death, and their function is related to their types. The apoptosis proteins are categorized into the following four types: (1) Cytoplasmic protein; (2) Plasma membrane-bound protein; (3) Mitochondrial inner and outer proteins; (4) Other proteins. A novel method, the Hilbert-Huang transform, is applied for predicting the type of a given apoptosis protein with support vector machine. High success rates were obtained by the re-substitute test (98/98=100%), jackknife test (91/98 = 92.9%).

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SHI, F. , CHEN, Q. and LI, N. (2008) Hilbert Huang Transform for Predicting Proteins Subcellular Location. Journal of Biomedical Science and Engineering, 1, 59-63. doi: 10.4236/jbise.2008.11009.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Zhou P., Chou J. J, Olea RS, Yuan J., G. Wagner. “Solution structure of Apaf-1 CARD and its interaction with caspase-9 CARD: a structural basis for specific adaptor/caspase interaction”. Proc Natl Acad Sci USA 96,1999,pp.11265–11270.
[2] Kerr J.F., Wyllie A. H., A. R. Currie. “Apoptosis: a basic biological phenomenon with wide-ranging implications in tissue kinetics”. Br J Cancer26,1972,pp.239–257.
[3] Schulz J.B., Weller M., M.A. Moskowitz. “Caspases as treatment targets in stroke and neurodegenerative diseases”. Ann Neurol 45,1999,pp.421–429.
[4] Barinaga M. “Stroke-damaged neurons may commit cellular suicide”. Science 281,1998, pp.1302–1303.
[5] Chou K C.. “A new branch of proteomics: prediction of protein cellular attributes”. In Gene Cloning and Expression Technologies (Weinrer, P.W. and Lu, Q., eds.), Eaton Publishing, Westborough, MA.,2002, Chapter 4,pp. 57-70,
[6] Huang J., F. Shi. “Support vector machines for prodicting apoptosis proteins types”. Acta bioinformatics 53,2005,pp. 39-47.
[7] Zhou G. P., Doctor. K. “Subcelluar location of Apoptosis proteins. Proteins:Structure”, Function, and Genetic 50,2003,pp.44-48.
[8] Chou K C.A. “novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space”. Proteins:Structure, Function and Genetics 21,1995,pp.319-344.
[9] Chou K.C. “ Prediction of protein cellular attributes using pseudo-amino-acid-composition”. Proteins :Structure, Function, and Genetics 43,2001,pp.246–255 (Erratum:ibid., 2001, vol. 44, 60).
[10] Chou J.J. Li H., Salvesen G.S., Yuan J., G. Wagner. “Solution structure of BID, an intracellular amplifier of apoptotic signaling”. Cell 96,1999,pp.615–624.
[11] Cai Y.D, Liu X.J., K.C. Chou. “Artificial neural network model for predicting membrane protein types”, J. Biomol. Struct. Dyn. 18,2001,pp. 607–610.
[12] Feng Z.P.. “Prediction of the subcellular location of prokaryotic proteins based on a new representation of the amino acid composition”. Biopolymers 58,2001,pp. 491-499.
[13] Cai Y. D,K.C.Chou. “Nearest neighbour algorithm for predicting protein subcellular location by combing functional domain composition and pseudo-amino acid composition”n. Biochem Biophys Res Comm 305,2003,pp.407-411.
[14] Huang N.E., Z. Shen, S.R. Long, M.L. Wu, H.H. Shih, Q. Zheng, N.C. Yen, C.C. Tung and H.H. Liu, “The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis”,Proc. Roy. Soc. London A, Vol. 454,1998, pp. 903–995.
[15] Vapnik V. “Statistical Learning Theory”. Wiley–Interscience. New York. 1998.
[16] Ding C.H., I. Dubchak. “Multi–class protein fold recognition using support vector machines and neural networks”. Bioinformatics 17,2001,pp. 349–358.
[17] Cai Y.D, Liu X.J., Xu X.B., K.C.Chou. “Support vector machines for prediction of protein subcellular location by incorporating quasi–sequence–order effect”. J. Cell. Biochem. 84, 2002b,pp. 343–348.
[18] Hua S. J., Z. R. Sun. “Support vector machine approach for protein subcellular localization prediction”. Bioinformatics 17,2001a,pp. 721–728.
[19] Hua S. J., Z.R. Sun. “A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach”. J. Mol. Biol. 308,2001b,pp. 397–407.

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