A tri-gram based feature extraction technique using linear probabilities of position specific scoring matrix for protein fold recognition

IEEE Transactions on Nanobioscience

Volumn 13, Issue 1, 2014, Pages 44-50

  Paliwal, Kuldip K ^a   Sharma, Alok ^a,b   Lyons, James ^a   Dehzangi, Abdollah ^a,c  

^a GRIFFITH UNIVERSITY   (Australia)

^b School of Engineering and Physics University of the South Paci'c   (Fiji)

^c CSIRO   (Australia)

Author keywords

Feature extraction technique; position specific scoring matrix (PSSM); protein fold recognition; support vector machine (SVM); tri gram

Indexed keywords

PROTEIN FOLDING; PROTEINS; SUPPORT VECTOR MACHINES; THREE DIMENSIONAL;

BENCHMARK DATASETS; FEATURE EXTRACTION TECHNIQUES; IDENTIFICATION OF PROTEINS; POSITION SPECIFIC SCORING MATRIX; PROTEIN FOLD RECOGNITION; RECOGNITION ACCURACY; THREE-DIMENSIONAL STRUCTURE; TRI GRAMS;

FEATURE EXTRACTION;

PROTEIN;

ARTICLE; CHEMISTRY; PROTEIN FOLDING; SEQUENCE ANALYSIS; SUPPORT VECTOR MACHINE;

PROTEIN FOLDING; PROTEINS; SEQUENCE ANALYSIS, PROTEIN; SUPPORT VECTOR MACHINES;

EID: 84896293227     PISSN: 15361241     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNB.2013.2296050     Document Type: Article

References (51)

1. I. Dubchak, I. Muchnik, and S. K. Kim, "Protein folding class predictor for SCOP: approach based on global descriptors," in Proc. 5th Int. Conf. Intell. Syst. Mol. Biol., 1997, pp. 104-107.
2. A. Chinnasamy, W. K. Sung, and A. Mittal, "Protein structure and fold prediction using tree-augmented naive Bayesian classifier," J. Bioinformat. Comp. Biol., vol. 3, no. 4, pp. 803-819, 2005. (Pubitemid 41015289)
3. K. L. Lin, C. Y. Lin, C.D. Huang, H. M. Chang, C.Y. Yang, C. T. Lin, C. Y. Tang, and D. F. Hsu, "Feature selection and combination criteria for improving accuracy in protein structure prediction," IEEE Trans. NanoBiosci., vol. 6, no. 2, pp. 186-196, 2007. (Pubitemid 46883425)
4. Y. Krishnaraj and C. K. Reddy, "Boosting methods for protein fold recognition: an empirical comparison," in Proc. IEEE Int. Conf. Bioinformat. Biomed., 2008, pp. 393-396.
5. I. K. Valavanis, G.M. Spyrou, and K. S. Nikita, "A comparative study of multi-classification methods for protein fold recognition," Int. J. Comput. Intell. Bioinformat. Syst. Biol., vol. 1, no. 3, pp. 332-346, 2010.
6. C. Ding and I. Dubchak, "Multi-class protein fold recognition using support vectormachines and neural networks," Bioinformatics, vol. 17, no. 4, pp. 349-358, 2001. (Pubitemid 32421929)
7. A. Dehzangi, S. P. Amnuaisuk, K. H. Ng, and E. Mohandesi, "Protein fold prediction problem using ensemble of classifiers," in Proc. 16th Int. Conf. Neural Inf. Process. II, 2009, pp. 503-511.
8. K. Kavousi, B.Moshiri, M. Sadeghi, B. N.Araabi, and A.A. Moosavi- Movahedi, "A protein fold classier formed by fusing different modes of pseudo amino acid composition via PSSM," Comput. Biol. Chem., vol. 35, no. 1, pp. 1-9, 2011.
9. V. Kecman and T. Yang, "Protein fold recognition with adaptive local hyper plane algorithm," in Proc IEEE Symp. Comput. Intell. Bioinformat. Comput. Biol. (CIBCB '09), pp. 75-78.
10. W. Chmielnicki and K. Stapor, "A hybrid discriminative-generative approach to protein fold recognition," Neurocomputing, vol. 75, pp. 194-198, 2012.
11. A. Dehzangi and S. P.Amnuaisuk, "Fold prediction problem: the application of new physical and physicochemical-based features," Protein Peptide Lett., vol. 18, pp. 174-185, 2011.
12. A. Sharma, S. Imoto, and S. Miyano, "A top-r feature selection algorithm for microarray gene expression data," IEEE/ACM Trans. Comput. Biol. Bioinformat., vol. 9, no. 3, pp. 754-764, 2012.
13. A. Sharma, S. Imoto, S. Miyano, and V. Sharma, "Null space based feature selectionmethod for gene expression data," Int. J. Mach. Learn. Cybern., vol. 3, no. 4, pp. 269-276, 2012b.
14. H. Zhang, T. Zhang, J.Gao, J. Ruan, S. Shen, and L.A.Kurgan, "Determination of protein folding kinetic types using sequence and predicted secondary structure and solvent accessibility," Amino Acids, pp. 1-13, 2010.
15. R. Najmanovich, J. Kuttner, V. Sobolev, andM. Edelman, "Side-chain flexibility in proteins upon ligand binding," Proteins: Struct., Funct., Bioinformat., vol. 39, no. 3, pp. 261-268, 2000. (Pubitemid 30226508)
16. J. T. Huang and J. Tian, "Amino acid sequence predicts folding rate for middle-size two-state proteins," Proteins: Struct., Funct., Bioinformat., vol. 63, no. 3, pp. 551-554, 2006.
17. T. L. Zhang, Y. S. Ding, and K. C. Chou, "Prediction protein structural classeswith pseudo amino acid composition: approximate entropy and hydrophobicity pattern," Theoretical Biol., vol. 250, pp. 186-193, 2008.
18. A. Sharma, K. K. Paliwal, A. Dehzangi, J. Lyons, S. Imoto, and S. Miyano, "A strategy to select suitable physicochemical attributes of amino acids for protein fold recognition," BMC Bioinformat., vol. 14, no. 233, pp. 1-11, 2013.
19. Y. H. Taguchi and M. M. Gromiha, "Application of amino acid occurrence for discriminating different folding types of globular proteins," BMC Bioinformat., vol. 8, p. 404, 2007.
20. M. T. A. Shamim, M. Anwaruddin, and H. A. Nagarajaram, "Support vectormachine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs," Bioinformatics, vol. 23, no. 24, pp. 3320-3327, 2007. (Pubitemid 350238884)
21. P. Ghanty and N. R. Pal, "Prediction of protein folds: extraction of new features dimensionality reduction, and fusion of heterogeneous classifiers," IEEE Trans. NanoBiosci., vol. 2, no. 4, pp. 100-110, 2009.
22. T. Yang, V. Kecman, L. Cao, C. Zhang, and J. Z. Huang, "Marginbased ensemble classifier for protein fold recognition," Expert Syst. Appl., vol. 38, pp. 12348-12355, 2011.
23. K. C. Chou, "Prediction of protein cellular attributes using pseudo amino acid composition," Proteins, vol. 43, pp. 246-255, 2001, erratum: 2001, vol. 44, 60.
24. Q. Dong, S. Zhou, and J. Guan, "A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation," Bioinformatics, vol. 25, no. 20, pp. 2655-2662, 2009.
25. H. B. Shen and K. C. Chou, "Ensemble classier for protein fold pattern recognition," Bioinformatics, vol. 22, pp. 1717-1722, 2006.
26. L. A. Kurgan, T. Zhang, H. Zhang, S. Shen, and J. Ruan, "Secondary structure-based assignment of the protein structural classes," Amino Acids, vol. 35, pp. 551-564, 2008.
27. T. Liu, X. Geng, X. Zheng, R. Li, and J. Wang, "Accurate prediction of protein structural class using auto covariance transformation of PSIBLAST profiles," Amino Acids, vol. 42, pp. 2243-2249, 2012.
28. A. Dehzangi, K. K. Paliwal, A. Sharma, O. Dehzangi, and A. Sattar, "A combination of feature extraction methods with an ensemble of different classifiers for protein structural class prediction problem," IEEE Trans. Comput. Biol. Bioinformat., 2013, to be published.
29. A. Sharma, J. Lyons, A. Dehzangi, and K. K. Paliwal, "A feature extraction technique using bi-gram probabilities of position specific scoring matrix for protein fold recognition," J. Theoretical Biol., vol. 320, no. 7, pp. 41-46, 2013.
30. K. C. Chou and H. B. Shen, "Foldrate: A web-server for predicting protein folding rates from primary sequence," Open Bioinformat. J., vol. 3, no. 2, pp. 31-50, 2009.
31. L. Nanni, J. Y. Shi, S. Brahnam, and A. Luminim, "Protein classification using texture descriptors extracted from the protein backbone image," J. Theoretical Biol., vol. 264, no. 3, pp. 1024-1032, 2010.
32. L. Nanni, A. Lumini, and A. Brahnam, "An empirical study on the matrix-based protein representations and their combination with sequence- based approaches," Amino Acids, vol. 44, no. 3, pp. 887-901, 2013.
33. H. B. Shen, J. N. Song, and K. C. Chou, "Prediction of protein folding rates from primary sequence by fusing multiple sequential features," J. Biomed. Sci. Eng., vol. 2, pp. 136-143, 2009.
34. A. Sharma and K. K. Paliwal, "Cancer classification by gradient LDA technique using microarray gene expression data," Data Knowl. Eng., vol. 66, no. 2, pp. 338-347, 2008.
35. P. Klein, "Prediction of protein structural class by discriminant analysis," BiochimBiopjysActa, vol. 874, pp. 205-215, 1986.
36. Z. Z. Wang and Z. Yuan, "How good is prediction of protein-structural class by the component-coupled method?," Proteins, vol. 38, pp. 165-175, 2000. (Pubitemid 30051416)
37. Y. S. Ding and T. L. Zhang, "Using Chou's pseudo amino acid composition to predict subcellular localization of apoptosis proteins: an approach with immune genetic algorithm-based ensemble classifier," Pattern Recog. Lett., vol. 29, pp. 1887-1892, 2008.
38. D. Bouchaffra and J. Tan, "Protein fold recognition using a structural hidden Markov model," in Proc. 18th Int. Conf. Pattern Recog., 2006, pp. 186-189.
39. P. Deschavanne and P. Tuffery, "Enhanced protein fold recognition using a structural alphabet," Proteins: Struct., Funct., Bioinformat., vol. 76, pp. 129-137, 2009.
40. K. Chen, X. Zhang, M. Q. Yang, and J. Y. Yang, "Ensemble of probabilistic neural networks for protein fold recognition," in Proc. 7th IEEE Int. Conf. Bioinformat. and Bioengineering (BIBE), 2007, pp. 66-70.
41. Y. Ying, K. Huang, and C. Campbell, "Enhanced protein fold recognition through a novel data integration approach," BMC Bioinformat., vol. 10, no. 1, 267, 2009.
42. A. Dehzangi, K. K. Paliwal, J. Lyons, A. Sharma, and A. Sattar, "Enhancing protein fold prediction accuracy using evolutionary and structural features," in Eighth IAPR Int. Conf. Pattern Recognition in Bioinformat. (PRIB), LNBI, 2013, vol. 7986, pp. 196-207.
43. A. Dehzangi, K. K. Paliwal, J. Lyons, A. Sharma, andA. Sattar, "Asegmentation- based method to extract structural and evolutionary features for protein fold recognition," IEEE Trans. Comput. Biol. Bioinformat., 2013, to be published.
44. A. Dehzangi, S. P. Amnuaisuk, and O. Dehzangi, "Enhancing protein fold prediction accuracy by using ensemble of different classifiers," Australian J. Intell. Inform. Process. Syst., vol. 26, no. 4, pp. 32-40, 2010.
45. A. Dehzangi and S. Karamizadeh, "Solving protein fold prediction problem using fusion of heterogeneous classifiers," Inform. Int. Interdisciplinary J., vol. 14, no. 11, pp. 3611-3622, 2011.
46. F. Markowetz, L. Edler, and M. Vingron, "Support vector machines for protein fold class prediction," Biometrical J., vol. 45, no. 3, pp. 377-389, 2003. (Pubitemid 36571156)
47. Z. Isik, B. Yanikoglu, and U. Sezeman, "Protein structural class determination using support vector machines," in Proc. 19th Int. Symp. Comput. Inform. Sci. (ISCIS'04), 2004, pp. 82-89.
48. S. F. Altschul, T. L.Madden, A. A. Schaffer, J. H. Zhang, Z. Zhang, W. Miller, andD. J. Lipman, "Gapped blast and psi-blast: a new generation of protein database search programs," Nucleic Acids Res., vol. 17, pp. 3389-3402, 1997. (Pubitemid 27359211)
49. V. N. Vapnik, The Nature of Statistical Learning Theory. NewYork: Springer-Verlag, 1995.
50. C. M. Bishop, Pattern Recognition and Machine Learning. New York: Springer Science, 2006.
51. C. C. Chang and C. J. Lin, "LIBSVM: a library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 271-2727, 2011.