Predicting the subcellular location of apoptosis proteins based on recurrence quantification analysis and the Hilbert–Huang transform

doi:10.1088/1674-1056/20/10/100504

中国物理B ›› 2011, Vol. 20 ›› Issue (10): 100504-100504.doi: 10.1088/1674-1056/20/10/100504

Predicting the subcellular location of apoptosis proteins based on recurrence quantification analysis and the Hilbert–Huang transform

Anh Vo¹, 韩国胜², 喻祖国³

(1)Discipline of Mathematical Science, Faculty of Science and Technology, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland 4001, Australia; (2)School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China; (3)School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China; Discipline of Mathematical Science, Faculty of Science and Technology, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland 4001, Australia

收稿日期:2011-06-02 修回日期:2011-06-15 出版日期:2011-10-15 发布日期:2011-10-15
基金资助:
Project supported by the National Natural Science Foundation of China (Grant No. 11071282), the Chinese Program for New Century Excellent Talents in University (Grant No. NCET-08-06867), the Natural Science Foundation of Hunan Province of China (Grant No. 10JJ7001), the Lotus Scholars Program of Hunan Province of China, the Aid Program for Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province of China, the Australian Research Council (Grant No. DP0559807), and the Postgraduate Research and Innovation Project of Hunan Province of China (Grant No. CX2010B243).

Predicting the subcellular location of apoptosis proteins based on recurrence quantification analysis and the Hilbert–Huang transform

Han Guo-Sheng(韩国胜)^a), Yu Zu-Guo(喻祖国)^a)b), and Anh Vo^b)

^a School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China; ^b Discipline of Mathematical Science, Faculty of Science and Technology, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland 4001, Australia

Received:2011-06-02 Revised:2011-06-15 Online:2011-10-15 Published:2011-10-15
Supported by:
Project supported by the National Natural Science Foundation of China (Grant No. 11071282), the Chinese Program for New Century Excellent Talents in University (Grant No. NCET-08-06867), the Natural Science Foundation of Hunan Province of China (Grant No. 10JJ7001), the Lotus Scholars Program of Hunan Province of China, the Aid Program for Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province of China, the Australian Research Council (Grant No. DP0559807), and the Postgraduate Research and Innovation Project of Hunan Province of China (Grant No. CX2010B243).

摘要/Abstract

摘要： Apoptosis proteins play an important role in the development and homeostasis of an organism. The elucidation of the subcellular locations and functions of these proteins is helpful for understanding the mechanism of programmed cell death. In this paper, the recurrent quantification analysis, Hilbert-Huang transform methods, the maximum relevance and minimum redundancy method and support vector machine are used to predict the subcellular location of apoptosis proteins. The validation of the jackknife test suggests that the proposed method can improve the prediction accuracy of the subcellular location of apoptosis proteins and its application may be promising in other fields.

关键词: apoptosis proteins, subcellular location, recurrent quantification analysis, Hilbert-Huang transform

Abstract: Apoptosis proteins play an important role in the development and homeostasis of an organism. The elucidation of the subcellular locations and functions of these proteins is helpful for understanding the mechanism of programmed cell death. In this paper, the recurrent quantification analysis, Hilbert-Huang transform methods, the maximum relevance and minimum redundancy method and support vector machine are used to predict the subcellular location of apoptosis proteins. The validation of the jackknife test suggests that the proposed method can improve the prediction accuracy of the subcellular location of apoptosis proteins and its application may be promising in other fields.

Key words: apoptosis proteins, subcellular location, recurrent quantification analysis, Hilbert-Huang transform

中图分类号: (Fractals)

05.45.Df

64.60.al (Fractal and multifractal systems) 87.15.Qt (Sequence analysis)

韩国胜, 喻祖国, Anh Vo. Predicting the subcellular location of apoptosis proteins based on recurrence quantification analysis and the Hilbert–Huang transform[J]. 中国物理B, 2011, 20(10): 100504-100504.

Han Guo-Sheng(韩国胜), Yu Zu-Guo(喻祖国), and Anh Vo . Predicting the subcellular location of apoptosis proteins based on recurrence quantification analysis and the Hilbert–Huang transform[J]. Chin. Phys. B, 2011, 20(10): 100504-100504.

参考文献 61

[1]	Jensen L J, Gupta R, Blom N, Devos D, Tamames J, Kesmir C, Nielsen H, Staerfeldt H H, Rapacki K, Workman C, Andersen C A F, Knudsen S, Krogh A, Valencia A and Brunak S 2002 J. Mol. Biol. 319 1257
[2]	Yu Z G, Xiao Q J, Shi L, Yu J W and Anh V 2010 Chin. Phys. B 19 068701
[3]	Zhu S M, Yu Z G and Anh V 2011 Chin. Phys. B 20 010505
[4]	Chou K C and Cai Y D 2004 Biochem. Biophys. Res. Commun. 320 1236
[5]	Reinhardt A and Hubbard T 1998 Nucleic Acids Res. 26 2230
[6]	Hua S J and Sun Z R 2001 Bioinformatics 17 721
[7]	Liu T G, Zheng X Q and Wang J 2010 Amino Acids 38 721
[8]	Huang Y and Li Y D 2001 Bioinformatics 20 21
[9]	Briesemeister S, Rahnenführer J and Kohlbacher O 2010 Bioinformatics 26 1232
[10]	Xiao X, Shao S H, Ding Y S, Huang Z D and Chou K C 2006 Amino Acids 30 49
[11]	Chou K C and Shen H B 2006 J. Cell Biochem. 99 517
[12]	Yuan Z 1999 FEBS Lett. 451 23
[13]	Xu Q, Hu D H, Xue H, Yu W C and Yang Q 2009 BMC Bioinformatics (Suppl. 1) 10 S47
[14]	Tung T Q and Lee D 2009 BMC Bioinformatics (Suppl. 1) 10 S43
[15]	Höglund A, Dönnes P, Blum T, Adolph H W and Kohlbacher O 2006 Bioinformatics 22 1158
[16]	Chou K C and Shen H B 2010 PLoS ONE 5 e9931
[17]	Jacobson M D, Weil M and Raff M C 1997 Cell 88 347
[18]	Kerr J F R, Wyllie A H and Currie A R 1972 Brit. J. Cancer 26 239
[19]	Adams J M and Cory S 1998 Science 281 1322
[20]	Evan G and Littlewood T 1998 Science 281 1317
[21]	Reed J C and Paternostro G 1999 Proc. Natl. Acad. Sci. USA 96 7614
[22]	Raff M 1998 Nature 396 119
[23]	Schulz J B, Weller M and Moskowitz M A 1999 Ann. Neurol. 45 421
[24]	Suzuki M, Youle R J and Tjandra N 2000 Cell 103 645
[25]	Zhou G P and Doctor K 2003 Proteins Struct. Funct. Genet. 50 44
[26]	Bulashevska A and Eils R 2006 BMC Bioinformatics 7 298
[27]	Zhang Z H, Wang Z H, Zhang Z R and Wang Y X 2006 FEBS Lett. 580 6169
[28]	Ding Y S and Zhang T L 2008 Pattern Recogn. Lett. 29 1887
[29]	Chen Y L and Li Q Z 2007 J. Theor. Biol. 245 775
[30]	Chen Y L and Li Q Z 2007 J. Theor. Biol. 248 377
[31]	Zhang L, Liao B, Li D C and Zhu W 2009 J. Theor. Biol. 259 361
[32]	Gu Q, Ding Y S, Jiang X Y and Zhang T L 2010 Amino Acids 38 975
[33]	Yu Z G, Anh V, Lau K S and Zhou L Q 2006 Phys. Rev. E 73 031920
[34]	Selz K A, Mandell A J and Shlesinger M F 1998 Biophys. J. 75 2332
[35]	Bordo D and Argos P 1991 J. Mol. Biol. 217 721
[36]	Webber J C L and Zbilut J P 1994 J. Appl. Physiol. 76 965
[37]	Huang N E, Shen Z, Long S R, Wu M C, Shih H H, Zheng Q A, Yen N C, Tung C C and Liu H H 1998 Proc. R. Soc. 454 903
[38]	Peng H, Long F and Ding C 2005 IEEE Tran. Pattern Anal. 27 1226
[39]	Chou K C and Shen H B 2007 Anal. Biochem. 370 1
[40]	Chou K C 1995 Proteins Struct. Funct. Genet. 21 319
[41]	Nakashima H and Nishikawa K 1994 J. Mol. Biol. 238 54
[42]	Cedano J, Aloy P, Perez-Pons J A and Querol E 1997 J. Mol. Biol. 266 594
[43]	Eckmann J P, Kamphorst S O and Ruelle D 1987 Europhys. Lett. 4 973
[44]	Riley M A and van Orden G C 2005, Retrieved March 1, 2005, online available from website http:// www.nsf.gov/sbe/bcs/pac/nmbs/nmbs.jsp
[45]	Giuliani A, Benigni R, Zbilut J P, Webber J C L, Sirabella P and Colosimo A 2002 Chem. Rev. 102 1471
[46]	Marwan N, Romano M C, Thiel M and Kurths J 2007 Phys. Rep. 438 237
[47]	Yang J Y, Peng Z L, Yu Z G, Zhang R J, Anh V and Wang D S 2009 J. Theor. Biol. 257 618
[48]	Giuliani A, Sirabella P, Benigni R and Colosimo A 2000 Protein Eng. 13 671
[49]	Manetti C, Ceruso M A, Giuliani A, Webber J C L and Zbilut J P 1999 Phys. Rev. E 59 992
[50]	Webber J C L, Giuliani A, Zbilut J P and Colosimo A 2001 Proteins 44 292
[51]	Zhou Y, Yu Z G and Anh V 2007 Phys. Lett. A 368 314
[52]	Yang J Y, Peng Z L and Chen X 2010 BMC Bioinformatics (Suppl. 1) 11 S9
[53]	Cao L 1997 Physica D 110 43
[54]	Huang N E, Wu M L C, Long S R, Shen S S P, Qu W D and Gloersen P and Fan K L 2003 Proc. R. Soc. 459 2317
[55]	Yu Z G, Anh V, Wang Y, Mao D and Wanliss J 2010 J. Geophys. Res. 115 A10219
[56]	Shi F, Chen Q J and Li N N 2008 J. Biomed. Sci. Eng. 1 59
[57]	Huang T, Shi X H, Wang P, He Z S, Feng K Y, Hu L L, Kong X Y, Li Y X, Cai Y D and Chou K C 2010 PLoS ONE 5 e10972
[58]	Vapnik V N 1995 The Nature of Statistical Learning Theory (Berline: Springer)
[59]	Chang C C and Lin C J LIBSVM: a Library for Support Vector Machines Software available online at http://www.csie.ntu.edu.tw/cjlin/libsvm
[60]	Shi J Y, Zhang S W, Pan Q, Cheng Y M and Xie J 2007 Amino Acids 33 69
[61]	Huang J, Shi F and Zhou H B 2005 China J. Bioinformatics 3 121

Predicting the subcellular location of apoptosis proteins based on recurrence quantification analysis and the Hilbert–Huang transform

Predicting the subcellular location of apoptosis proteins based on recurrence quantification analysis and the Hilbert–Huang transform

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