Protein fold recognition using geometric kernel data fusion

Bioinformatics

Volumn 30, Issue 13, 2014, Pages 1850-1857

  Zakeri, Pooya ^a   Jeuris, Ben ^a   Vandebril, Raf ^a   Moreau, Yves ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEIN;

ALGORITHM; AUTOMATED PATTERN RECOGNITION; CHEMISTRY; COMPUTER PROGRAM; METABOLISM; POSITION WEIGHT MATRIX; PROCEDURES; PROTEIN FOLDING;

ALGORITHMS; PATTERN RECOGNITION, AUTOMATED; POSITION-SPECIFIC SCORING MATRICES; PROTEIN FOLDING; PROTEINS; SOFTWARE;

EID: 84903701959     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btu118     Document Type: Article

References (51)

1. Absil, P.A. et al. (2008) Optimization Algorithms on Matrix Manifolds. Princeton University Press
2. Apweiler, R. et al. (2001) The interpro database, an integrated documentation resource for protein families, domains and functional sites. Nucleic Acids Res., 29, 37-40. (Pubitemid 32054401
3. Arsigny, V. et al. (2007) Geometric means in a novel vector space structure on symmetric positive-definite matrices. SIAM J. Matrix Anal. Appl., 29, 328-347
4. Bach, F.R. et al. (2004) Multiple kernel learning, conic duality, and the SMO algorithm. In: Proceedings of the 21st International Conference on Machine Learning (ICML). Omnipress, Banff, Canada
5. Bhatia, R. (2007) Positive Definite Matrices. Princeton Series in Applied Mathematics. Princeton University Press, Princeton
6. Cai, Y.D. et al. (2002) Support vector machines for prediction of protein subcellular location by incorporating quasi-sequence-order effect. J. Cell. Biochem., 84, 343-348
7. Cai, Y.D. et al. (2003) Support vector machines for predicting membrane protein types by using functional domain composition. Biophys. J., 84, 3257-3263
8. Chang, C.C. and Lin, C.J. (2011) Libsvm: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2, 27.1-27.27
9. Chen, K. and Kurgan, L. (2007) Pfres: Protein fold classification by using evolutionary information and predicted secondary structure. Bioinformatics, 23, 2843-2850. (Pubitemid 350162889
10. Chou, K.C. (2001) Prediction of protein cellular attributes using pseudo-Amino acid composition. Proteins, 43, 246-255
11. Chou, K.C. and Cai, Y.D. (2004) Predicting protein structural class by functional domain composition. Biochem. Biophys. Res. Commun., 321, 1007-1009
12. Daemen, A. et al. (2009) A kernel-based integration of genome-wide data for clinical decision support. Genome Med., 1, 39
13. Damoulas, T. and Girolami, M.A. (2008) Probabilistic multi-class multi-kernel learning: On protein fold recognition and remote homology detection. Bioinformatics, 24, 1264-1270. (Pubitemid 351659623
14. De Bie, T. et al. (2007) Kernel-based data fusion for gene prioritization. Bioinformatics, 23, i125-i132. (Pubitemid 47244393
15. Ding, C.H. and Dubchak, I. (2001) Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics, 17, 349-358. (Pubitemid 32421929
16. Dubchak, I. et al. (1995) Prediction of protein folding class using global description of amino acid sequence. Proc. Natl Acad. Sci. USA, 92, 8700-8704
17. Dubchak, I. et al. (1999) Recognition of a protein fold in the context of the scop classification. Proteins, 35, 401-407. (Pubitemid 29264836
18. Foster, D. and Phillips, G. (1984) The arithmetic-harmonic mean. Math. Comput., 42, 183-191
19. Gönen, M. and Alpaydin, E. (2011) Multiple kernel learning algorithms. J. Mach. Learn. Res., 12, 2211-2268
20. Hunter, S. et al. (2012) Interpro in 2011: New developments in the family and domain prediction Database. Nucleic Acids Res., 40, D306-D312
21. Jeuris, B. et al. (2012) A survey and comparison of contemporary algorithms for computing the matrix geometric mean. Electron. Trans. Numer. Anal., 39, 379-402
22. Kaur, H. and Raghava, G. (2003) A neural-network based method for prediction of gamma-Turns in proteins from multiple sequence alignment. Protein Sci., 12, 923-929. (Pubitemid 36505427
23. Kavousi, K. et al. (2011) A protein fold classifier formed by fusing different modes of pseudo amino acid composition via {PSSM}. Comput. Biol. Chem., 35, 1-9
24. Lanckriet, G.R.G. et al. (2004a) Learning the kernel matrix with semi-definite programming. J. Mach. Learn. Res., 5, 27-72
25. Lanckriet, G.R.G. et al. (2004b) A statistical framework for genomic data fusion. Bioinformatics, 20, 2626-2635. (Pubitemid 39530149
26. Liao, L. and Noble, W.S. (2003) Combining pairwise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships. J. Comput. Biol., 10, 857-868
27. Lin, C. et al. (2013) Hierarchical classification of protein folds using a novel ensemble classifier. PLoS One, 8, e56499
28. Marchler-Bauer, A. et al. (2007) CDD: A conserved domain Database for interactive domain family analysis. Nucleic Acids Res., 35 (Suppl. 1), D237-D240. (Pubitemid 46056206
29. Marchler-Bauer, A. et al. (2013) Cdd: Conserved domains and protein threedimensional structure. Nucleic Acids Res., 41, D348-D352
30. Murzin, A.G. et al. (1995) Scop: A structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol., 247, 536-540
31. Nanni, L. (2006) A novel ensemble of classifiers for protein fold recognition. Neurocomputing, 69, 2434-2437
32. Petersen, B. et al. (2009) A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct. Biol., 9, 51
33. Qiu, S. and Lane, T. (2009) A framework for multiple kernel support vector regression and its applications to sirna efficacy prediction. IEEE/ACM Trans. Comput. Biol. Bioinform., 6, 190-199
34. Rakotomamonjy, A et al. (2008) Simplemkl. 9, 2491-2521
35. Rangwala, H. and Karypis, G. (2005) Profile-based direct kernels for remote homology detection and fold recognition. Bioinformatics, 21, 4239-4247. (Pubitemid 41724303
36. Rashid, M. et al. (2007) Support vector machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs. BMC Bioinformatics, 8, 337
37. Schaffer, A.A. et al. (2001) Improving the accuracy of psi-blast protein Database searches with composition-based statistics and other refinements. Nucleic Acids Res., 29, 2994-3005. (Pubitemid 32685225
38. Sharma, A. et al. (2013) A feature extraction technique using bi-gram probabilities of position specific scoring matrix for protein fold recognition. J. Theor. Biol., 320, 41-46
39. Shen, H.B. and Chou, K.C. (2006) Ensemble classifier for protein fold pattern recognition. Bioinformatics, 22, 1717-1722
40. Shen, H.B. and Chou, K.C. (2007) Nuc-ploc: A new web-server for predicting protein subnuclear localization by fusing pseaa composition and psepssm. Protein Eng. Des. Sel., 20, 561-567
41. Shen, H.B. and Chou, K.C. (2009) Predicting protein fold pattern with functional domain and sequential evolution information. J. Theor. Biol., 256, 441-446
42. Sonnenburg, S. et al. (2006) Large scale multiple kernel learning. J. Mach. Learn. Res., 7, 1531-1565
43. Vishwanathan, S.V.N. et al. (2010)Multiple kernel learning and the SMO algorithm. In: Advances in Neural Information Processing Systems. Vancouver, Canada
44. Wainer, H. (1976) Estimating coefficients in linear models: It don't make no nevermind. Psychol. Bull., 83, 213-217
45. Xie, D. et al. (2005) Locsvmpsi: A web server for subcellular localization of eukaryotic proteins using svm and profile of psi-blast. Nucleic Acids Res., 33 (Suppl. 2), W105-W110. (Pubitemid 44529889
46. Yang, J.Y. and Chen, X. (2011) Improving taxonomy-based protein fold recognition by using global and local features. Proteins, 79, 2053-2064
47. Yang, T. et al. (2011) Margin-based ensemble classifier for protein fold recognition. Expert Syst. Appl., 38, 12348-12355
48. Ying, Y. et al. (2009) Enhanced protein fold recognition through a novel data integration approach. BMC Bioinformatics, 10, 267
49. Yu, S. et al. (2011) Kernel-based Data Fusion for Machine Learning - Methods and Applications in Bioinformatics and Text Mining. vol. 345 of Studies in Computational Intelligence, Springer, Heidelberg
50. Zakeri, P. et al. (2011) Prediction of protein submitochondria locations based on Data fusion of various features of sequences. J. Theor. Biol., 269, 208-216
51. Zien, A. and Ong, C.S. (2007) Multiclass multiple kernel learning. In: Proceedings of the 24th international conference on Machine learning, ICML'07. New York, NY: ACM, pp. 1191-1198.