High performance set of PseAAC and sequence based descriptors for protein classification

Journal of Theoretical Biology

Volumn 266, Issue 1, 2010, Pages 1-10

  Nanni, Loris ^a   Brahnam, Sheryl ^b   Lumini, Alessandra ^a  

^a UNIVERSITY OF BOLOGNA   (Italy)

^b MISSOURI STATE UNIVERSITY   (United States)

Author keywords

Ensemble of classifiers, support vector machines; Machine learning; Proteins classification

Indexed keywords

ADHESIN; AMINO ACID; BACTERIAL PROTEIN; G PROTEIN COUPLED RECEPTOR; MEMBRANE PROTEIN; VIRUS PROTEIN;

AMINO ACID; ARTIFICIAL NEURAL NETWORK; DATA SET; LEARNING; PROTEIN;

AMINO ACID ANALYSIS; AMINO ACID SEQUENCE; ARTICLE; AUTOMATION; MATHEMATICAL MODEL; PEPTIDE ANALYSIS; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN DOMAIN; PROTEIN PROTEIN INTERACTION; RELIABILITY; SEQUENCE ANALYSIS; SUPPORT VECTOR MACHINE; VALIDATION STUDY;

EID: 77953934865     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2010.06.006     Document Type: Article

References (67)

1. Bairoch A., Apweiler R. The SWISS-PROT protein sequence database and its supplement TrEMBL. Nucleic Acids Research 2000, 28:45-48.
2. Ben-Ga L.I., Shani A., Gohr A., Grau J., Arviv S., Shmilovici A., Posch S., Grosse I. Identification of transcription factor binding sites with variable-order Bayesian networks. Bioinformatics 2005, 21:2657-2666.
3. Bock J., Gough D. Whole-proteome interaction mining. Bioinformatics 2003, 19:125-135.
4. Chen C., Chen L., Zou X., Cai P. Prediction of protein secondary structure content by using the concept of Chou's pseudo amino acid composition and support vector machine. Protein & Peptide Letters 2009, 16:27-31.
5. Chen L., Yang J., Yu J., Yao Z., Sun L., Shen Y., Jin Q. VFDB: a reference database for bacterial virulence factors. Nucleic Acids Research 2005, 33:D325-D328.
6. Chou K.C. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins: Structure, Function, and Genetics 2001, 43:246-255.
7. Chou K.C. Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Current Proteomics 2009, 6:262-274.
8. Chou K.C., Zhang C.T. Review: prediction of protein structural classes. Critical Reviews in Biochemistry and Molecular Biology 1995, 30:275-349.
9. Chou K.C., Shen H.B. Review: recent progresses in protein subcellular location prediction. Analytical Biochemistry 2007, 370.
10. Chou K.C., Shen H.B. Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. Biochemical and Biophysical Research Communications 2007, 357:633-640.
11. Chou K.C., Shen H.B. MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochemical and Biophysical Research Communications 2007, 360:339-345.
12. Chou K.C., Shen H.B. Cell-PLoc: a package of web-servers for predicting subcellular localization of proteins in various organisms. Nature Protocols 2008, 3:153-162.
13. Chou K.C., Shen H.B. ProtIdent: a web server for identifying proteases and their types by fusing functional domain and sequential evolution information. Biochemical and Biophysical Research Communications 2008, 376:321-325.
14. Chou, K.C., Shen, H.B., 2009. Review: recent advances in developing web-servers for predicting protein attributes. Natural Science2, 63-92 (openly accessible at 〈〉). http://www.scirp.org/journal/NS/.
15. Chou K.C., Shen H.B. A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mP Loc 2.0. PLoS ONE 2010, 5:e9931.
16. Cristianini N., Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods 2000, Cambridge University Press, Cambridge, UK.
17. Daras P., Zarpalas D., Axenopoulos A., Tzovaras D., Strintzis M.G. Three-dimensional shape-structure comparison method for protein classification. IEEE Transactions on Computational Biology and Bioinformatics 2006, 3:193-207.
18. Ding Y.S., Zhang T.L. Using Chou's pseudo amino acid composition to predict subcellular localization of apoptosis proteins: an approach with immune genetic algorithm-based ensemble classifier. Pattern Recognition Letters 2008, 29:1887-1892.
19. Esmaeili M., Mohabatkar H., Mohsenzadeh S. Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses. Journal of Theoretical Biology 2010, 263:203-209.
20. Fawcett T. ROC Graphs: Notes and Practical Considerations for Researchers 2004, HP Laboratories, Palo Alto, USA.
21. Garg A., Gupta D. VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens. BMC Bioinformatics 2008, 9. 10.1186/1471-2105-9-62.
22. Guo, J., Lin, Y., Sun, Z., 2005. A novel method for protein subcellular localization: combining residue-couple model and SVM. In: Proceedings of 3rd Asia-Pacific Bioinformatics Conference, pp. 117-129.
23. He Z.S., Zhang J., Shi X.H., Hu L.L., Kong X.G., Cai Y.D., Chou K.C. Predicting drug-target interaction networks based on functional groups and biological features. PLoS ONE 2010, 5:e9603.
24. Ho T.K. The random subspace method for constructing decision forests. IEEE PAMI 1998, 20:832-844.
25. Hu, J., Zhang F., Improving Protein Localization Prediction Using Amino Acid Group Based Physichemical Encoding, BICoB 2009, LNBI 5462, 2009, pp. 248-258.
26. Jaakkola, T., Diekhans, M., Haussler, D., 1999. Using the fisher kernel method to detect remote protein homologies. In: Seventh International Conference on Intelligent Systems for Molecular Biology. AAAI Press, pp. 149-158.
27. Jiang X., Wei R., Zhang T.L., Gu Q. Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy. Protein & Peptide Letters 2008, 15:392-396.
28. Kawashima S., Kanehisa M. AAindex: amino acid index database. Nucleic Acids Research 2000, 20.
29. Kittler J. On combining classifiers. IEEE PAMI 1998, 20:226-239.
30. Kumar, M., Verma, R., Raghava, G.P.S., 2006. Prediction of mitochondrial proteins using support vector machine and hidden Markov model. Journal of Biological Chemistry 281, 5357-5363.
31. Landgrebe T.C.W., Duin Robert P.W. Approximating the multiclass ROC by pairwise analysis. Pattern Recognition Letters 2007, 28:1747-1758.
32. Lei Z., Dai Y. An SVM-based system for predicting protein subnuclear localizations. BMC Bioinformatics 2005, 6.
33. Leslie C.S., Eskin E., Cohen A., Weston J., Noble W.S. Mismatch string kernels for discriminative protein classification. Bioinformatics 2004, 20:467-476.
34. Li F.M., Li Q.Z. Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach. Protein & Peptide Letters 2008, 15.
35. Li Z.-C., Zhou X.-B., Dai Z., Zou X.-Y. Prediction of protein structural classes by Chou's pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis. Amino Acids 2008, 22:699-705. 10.1007/s00726-008-0170-2.
36. Liao S., Law M.W.K., Chung A.C.S. Dominant local binary patterns for texture classification. IEEE Transactions on Image Processing 2009, 18:1107-1118.
37. Lin H. The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition. Journal of Theoretical Biology 2008, 252:350-356.
38. Lin H., Ding H., Feng-Biao Guo F.B., Zhang A.Y., Huang J. Predicting subcellular localization of mycobacterial proteins by using Chou's pseudo amino acid composition. Protein & Peptide Letters 2008, 15:739-744.
39. Lin, W.Z., Xiao, X., Chou, K.C., 2009. GPCR-GIA: a web-server for identifying G-protein coupled receptors and their families with grey incidence analysis. Protein Engineering, Design & Selection.
40. Martin S., Roe D., Faulon J.L. Predicting protein-protein interactions using signature products. Bioinformatics 2005, 21(2):218-226.
41. Murphy L.R., Wallqvist A., Levy R.M. Simplified amino acid alphabets for protein fold recognition and implications for folding. Protein Engineering 2000, 13:149-152.
42. Nanni L. Comparison among feature extraction methods for HIV-1 Protease Cleavage Site Prediction. Pattern Recognition 2006, 39:711-713.
43. Nanni L., Lumini A. An ensemble of K-Local Hyperplane for predicting protein-protein interactions. BioInformatics 2006, 22:1207-1210.
44. Nanni L., Lumini A. A genetic approach for building different alphabets for peptide and protein classification. BMC Bioinformatics 2008, 9.
45. Pudil P., Novovicova J., Kittler J. Floating search methods in feature selection. Pattern Recognition Letters 1994, 5:1119-1125.
46. Qin, Z.C., 2006. ROC analysis for predictions made by probabilistic classifiers. In: Fourth International Conference on Machine Learning and Cybernetics, vol. 5, pp. 3119-3312.
47. Qiu J.D., Huang J.H., Liang R.P., Lu X.Q. Prediction of G-protein-coupled receptor classes based on the concept of Chou's pseudo amino acid composition: an approach from discrete wavelet transform. Analytical Biochemistry 2009, 390:68-73.
48. Rodriguez J.J., Kuncheva L.I., Alonso C.J. Rotation forest: a new classifier ensemble method. IEEE PAMI 2006, 28:1619-1630.
49. Shen H.-B., Chou K.-C. Virus-PLoc: a fusion classifier for predicting the subcellular localization of viral proteins within host and virus-infected cells. Biopolymers 2007, 15:233-240.
50. Shen H.-B., Chou K.-C. Gpos-PLoc: an ensemble classifier for predicting subcellular localization of Gram-positive bacterial proteins. Protein Engineering Design & Selection 2007, 20:39-46.
51. Shen H.B., Chou K.C. Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins. Journal of Theoretical Biology 2010, 264:326-333.
52. Tan X., Triggs B. Enhanced local texture feature sets for face recognition under difficult lighting conditions. Analysis and Modelling of Faces and Gestures LNCS 2007, 4778:168-182.
53. Tantoso E., Li K.B. AAIndexLoc: predicting subcellular localization of proteins based on a new representation of sequences using amino acid indices. Amino Acids 2008, 35:345-353.
54. Verma R., Varshney G.C., Raghava G.P.S. Prediction of mitochondrial proteins of malaria parasite using split amino acid composition and PSSM profile. Amino Acids 2009, 10.1007/s00726-009-0381-1.
55. Xi L., Bo L., Yu S., Qingguang Z., Jiawei L. Protein functional class prediction using global encoding of amino acid sequence protein functional class prediction using global encoding of amino acid sequence. Journal of Theoretical Biology 2009, 261:290-293.
56. Xiao X., Lin W.Z. Application of protein grey incidence degree measure to predict protein quaternary structural types. Amino Acids 2009, 37:741-749.
57. Xiao X., Wang P., Chou K.C. Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image. Journal of Theoretical Biology 2008, 254:691-696.
58. Xiao X., Lin W.Z., Chou K.C. Using grey dynamic modeling and pseudo amino acid composition to predict protein structural classes. Journal of Computational Chemistry 2008, 29:2018-2024.
59. Xiao X., Wang P., Chou K.C. GPCR-CA: a cellular automaton image approach for predicting G-protein-coupled receptor functional classes. Journal of Computational Chemistry 2009, 30:1414-1423.
60. Xiao X., Wang P., Chou K.C. Predicting protein quaternary structural attribute by hybridizing functional domain composition and pseudo amino acid composition. Journal of Applied Crystallography 2009, 42:169-173.
61. Xiao, X., Shao, S.H., Huang, Z.D., Chou, K.C., 2006a. Using pseudo amino acid composition to predict protein structural classes: approached with complexity measure factor. Journal of Computational Chemistry.
62. Xiao X., Shao S.H., Ding Y.S., Huang Z.D., Chou K.C. Using cellular automata images and pseudo amino acid composition to predict protein subcellular location. Amino Acids 2006, 30:49-54.
63. Xiao X., Shao S., Ding Y., Huang Z., Huang Y., Chou K.C. Using complexity measure factor to predict protein subcellular location. Amino Acids 2005, 28:57-61.
64. Yang Z.R., Thomson R. Bio-basis function neural network for prediction of protease cleavage sites in proteins. IEEE Transactions on Neural Networks 2005, 16:263-274.
65. Zeng Y.H., Guo Y.Z., Xiao R.Q., Yang L., Yu L.Z., Li M.L. Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. Journal of Theoretical Biology 2009, 259:366-372.
66. Zhou G.P. An intriguing controversy over protein structural class prediction. Journal of Protein Chemistry 1998, 17:729-738.
67. Zhou X.B., Chen C., Li Z.C., Zou X.Y. Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes. Journal of Theoretical Biology 2007, 248:546-551.