Remote protein homology detection and fold recognition using two-layer support vector machine classifiers

Computers in Biology and Medicine

Volumn 41, Issue 8, 2011, Pages 687-699

  Muda, Hilmi M ^a   Saad, Puteh ^a   Othman, Razib M ^a  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

Author keywords

Bio inspired kernel.; Fold recognition; Remote protein homology detection; Support Vector Machines; Two layer classifiers

Indexed keywords

BIO-INSPIRED; FOLD RECOGNITION; REMOTE PROTEIN HOMOLOGY DETECTION; SUPPORT VECTOR; TWO LAYERS;

IMAGE RETRIEVAL; NEURAL NETWORKS; PROTEIN FOLDING; SUPPORT VECTOR MACHINES;

PROTEINS;

ARTICLE; KERNEL METHOD; MOLECULAR RECOGNITION; PRIORITY JOURNAL; PROTEIN FOLDING; SEQUENCE HOMOLOGY; STUDENT T TEST; SUPPORT VECTOR MACHINE;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; DATABASES, PROTEIN; HUMANS; PATTERN RECOGNITION, AUTOMATED; PROTEIN FOLDING; PROTEINS; PROTEOMICS; ROC CURVE; SEQUENCE ANALYSIS, PROTEIN; SEQUENCE HOMOLOGY, AMINO ACID;

EID: 79960639029     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2011.06.004     Document Type: Article

References (62)

1. Vendruscolo M., Dobson C.M. A glimpse at the organization of the protein universe. PNAS 2005, 102(16):5641-5642.
2. Honda M., Kawai H., Shirota Y., Yamashita T., Takamura T., Kaneko S. cDNA microarray analysis of autoimmune hepatitis, primary biliary cirrhosis and consecutive disease manifestation. Journal of Autoimmunity 2005, 25(2):133-140.
3. Boisvert S., Marchand M., Laviolette F., Corbeil J. HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels. Retrovirology 2008, 5(11):1-14.
4. Dong Q.W., Wang X.L., Lin L. Application of latent semantic analysis to protein remote homology detection. Bioinformatics 2006, 22(3):285-290.
5. Krogh A., Brown M., Mian I., Sjolander K., Haussler D. Hidden Markov models in computational biology: applications to protein modeling. Journal of Molecular Biology 1994, 235(5):1501-1531.
6. Oĝul H., Mumcuoĝlu E.U. A discriminative method for remote homology detection based on n-peptide compositions with reduced amino acid alphabets. Biosystems 2007, 87(1):75-81.
7. Liao L., Noble W.S. Combining pairwise sequence similarity and support vector machines for remote protein homology detection. Journal of Computational Biology 2002, 10(6):225-232.
8. Liao L., Noble W.S. Combining pairwise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships. Journal of Computational Biology 2003, 10(6):857-868.
9. Ohlson T., Wallner B., Elofsson A. Profile-profile methods provide improved fold-recognition: a study of different profile-profile alignment methods. Proteins 2004, 57(1):187-197.
10. Han S., Lee B.C., Yu S.T., Jeong C.S., Lee S., Kim D. Fold recognition by combining profile-profile alignment and support vector machine. Bioinformatics 2005, 21(11):2667-2673.
11. Zhou H., Zhou Y. Single-body residue-level knowledge-based energy score combined with sequence-profile and secondary structure information for fold recognition. Proteins: Structure, Function, and Bioinformatics 2004, 55(4):1005-1013.
12. Jung I., Lee J., Lee S.Y., Kim D. Application of nonnegative matrix factorization to improve profile-profile alignment features for fold recognition. BMC Bioinformatics 2008, 9(298):1-11.
13. Zhou H., Zhou Y. Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments. Proteins: Structure, Function, and Bioinformatics 2005, 58(2):321-328.
14. Ralf Z., Ralf T. Fast Protein Fold Recognition and Accurate Sequence-Structure Alignment, Lecture Notes in Computer Science (LNCS) 1997, vol. 1278. Springer, pp. 137-146.
15. McDonnell A.V., Menke M., Palmer N., King J., Cowen L., Berger B. Fold recognition and accurate sequence-structure alignment of sequences directing beta-sheet proteins. Proteins 2006, 63(4):976-985.
16. Koretke K.K., Russell R.B., Copley R.R., Lupas A.N. Fold recognition using sequence and secondary structure information. Proteins 1999, (Suppl. 3):141-148.
17. Andreeva A., Howorth D., Chandonia J.M., Brenner S.E., Hubbard T.J.P., Chothia C. Data growth and its impact on the SCOP database: new developments. Nucleic Acids Research 2008, 36(database issue):D419-D425.
18. Rangwala H., Karypis G. Building multiclass classifiers for remote homology detection and fold recognition. BMC Bioinformatics 2006, 7(455):455-471.
19. Liu B., Wang X., Lin L., Dong Q., Wang X. A discriminative method for protein remote homology detection and fold recognition combining top-n-grams and latent semantic analysis. BMC Bioinformatics 2008, 9(510):1-16.
20. Raval A., Ghahramani Z., Wild D.L. A Bayesian network model for protein fold and remote homologue recognition. Bioinformatics 2002, 18(6):788-801.
21. Chinnasamy A., Sung W.K., Mittal A. Protein homology structure and fold prediction using tree-augmented naÏve Bayesian classifier. Journal of Bioinformatics and Computational Biology 2005, 3(4):803-819.
22. Q.W. Dong, L. Lin, X.L. Wang, M.H. Li, A pattern-based SVM for remote homology detection, in: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, China, 18-21 August 2005, pp. 3363-3368.
23. Shah A.R., Oehmen C.S., Webb-Robertson B.J. SVM-HUSTLE-an iterative semi-supervised machine learning approach for pairwise protein remote homology detection. Bioinformatics 2008, 24(6):783-790.
24. Shamim M.T.A., Anwaruddin M., Nagarajaram H.A. Support vector machine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs. Bioinformatics 2007, 23(24):3320-3327.
25. Melvin I., Ie E., Kuang R., Weston J., Noble W.S., Leslie C. SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition. BMC Bioinformatics 2007, 8(Suppl. 4):S2.
26. Jaakkola T., Diekhans M., Haussler D. A discriminative framework for detecting remote protein homologies. Journal of Computational Biology 2000, 7(1):95-114.
27. C. Leslie, E. Eskin, W.S. Noble, The spectrum kernel: a string kernel for SVM protein classification, in: Proceedings of the Seventh Pacific Symposium on Biocomputing, Hawaii, USA, 3-7 January 2002, pp. 564-575.
28. Leslie C.S., Eskin E., Cohen A., Weston J., Noble W.S. Mismatch string kernels for discriminative protein classification. Bioinformatics 2004, 20(4):467-476.
29. Lingner T., Meinicke P. Remote homology detection based on oligomer distances. Bioinformatics 2006, 22(18):2224-2231.
30. B. Liu, L. Lin, X. Wang, Q. Dong, X. Wang, A discriminative method for protein remote homology detection based on N-nary profiles, in: Proceedings of the Second Bioinformatics Research and Development, Vienna, Austria, 7-9 July 2008, pp. 74-86.
31. Tan A.C., David G., Deville Y. Multi-class protein fold classification using a new ensemble machine learning approach. Genome Informatics 2003, 14(1):206-217.
32. Busuttil S., Abela J., Pace G.J. Support vector machines with profile-based kernels for remote protein homology detection. Genome Informatics 2004, 15(2):191-200.
33. T. Joachims, Training linear SVMs in linear time, in: Proceedings of the 12th ACM Conference on Knowledge Discovery and Data Mining, Philadelphia, USA, 20-23 August 2006, pp. 217-226.
34. Dong Q., Wang X., Lin L. Application of latent semantic analysis to protein remote homology detection. Bioinformatics 2006, 22(3):285-290.
35. Pearson W.R., Lipman D.J. Improved tools for biological sequence comparison. PNAS 1988, 85(8):2444-2448.
36. Zheng S., Tang H., Han Z., Zhang H. Solving large-scale multiclass learning problems via an efficient support vector classifier. Journal of Systems Engineering and Electronics 2006, 17(4):910-915.
37. Kawashima S., Pokarowski P., Pokarowska M., Kolinski A., Katayama T., Kanehisa M. AAindex: amino acids index database, progress report 2008. Nucleic Acids Research 2008, 36:D202-D205. (database issue).
38. Rifkin R., Klautau A. In defense of one-versus-all classification. Journal Machine Learning Research 2004, 5(1):101-104.
39. W. Waegeman, B.D. Baets, L. Boullart, A graph-theoretic approach for reducing one-versus-one multi-class classification to ranking, in: Proceedings of Sixth International Workshop on Mining and Learning with Graphs, Helsinki, Finland, 4-5 July 2008, pp. 1-3.
40. T. Joachims, A support vector method for multivariate performance measures, in: Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany, 7-11 August 2005, pp. 377-384.
41. Mangasarian O., Musicant D. Lagrangian support vector machines. Journal of Machine Learning Research 2001, 1(1):161-177.
42. Tsochantaridis I., Joachims T., Hofmann T., Altun Y. Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research 2005, 6(1):1453-1484.
43. Schoelkopf B., Smola A.J. Learning with Kernels 2002, MIT Press, Cambridge, MA.
44. Altschul S.F., Madden T.L., Schäffer A.A., Zhang J., Zhang Z., Miller W., Lipman D.J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research 1997, 25(17):3389-3402.
45. Rangwala H., Karypis G. Profile-based direct kernels for remote homology detection and fold recognition. Bioinformatics 2005, 21(23):4239-4247.
46. Melvin I., Ie E., Weston J., Noble W.S., Leslie C. Multi-class protein classification using adaptive codes. Journal of Machine Learning Research 2007, 8(1):1557-1581.
47. Saigo H., Vert J.-P., Ueda N., Akutsu T. Protein homology detection using string alignment kernels. Bioinformatics 2004, 20(11):1682-1689.
48. Smith T., Waterman M. Identification of common molecular sequences. Journal of Molecular Biology 1981, 147(1):195-197.
49. Watkins C. Dynamic Alignment Kernels 2000, MIT Press, Cambridge, MA.
50. Eddy S.R. Profile hidden Markov model. Bioinformatics 1998, 14(9):755-763.
51. Gartner T. A survey of kernels for structured data. ACM SIGKDD Explorations Newsletter 2003, 5(1):49-58.
52. D. Haussler, Convolution Kernels on Discrete Structures, Technical Report, University of California, Santa Cruz, 1999.
53. Girolami M. Mercer kernel-based clustering in feature space. IEEE Transactions on Neural Networks 2002, 13(3):780-784.
54. Eddy S.R. Where did the BLOSUM62 alignment score matrix come from?. Nature Biotechnology 2004, 22(8):1035-1036.
55. Kuang R., Ie E., Wang K., Siddiqi M., Freund Y., Leslie C. Profile kernels for detecting remote protein homologs and discriminative motifs. Journals of Bioinformatics and Computational Biology 2005, 3(3):527-550.
56. Dong Q., Wang X., Lin L. Application of latent semantic analysis to protein remote homology detection. Bioinformatics 2005, 22(3):285-290.
57. Bork P., Downing A.K., Kieffer B., Campbell I.D. Structure and distribution of modules in extracellular proteins. Quarterly Reviews of Biophysics 1996, 29:119-167.
58. Dasgupta A., Scovell W.M. TFIIA abrogates the effects of inhibition by HMGB1 but not E1A during the early stages of assembly of the transcriptional preinitiation complex. Biochimica et Biophysica Acta 2003, 1627(2-3):101-110.
59. Yu L.M. Elicitins from Phytophthora and basic resistance in tobacco. PNAS 1995, 92(10):4088-4094.
60. van Aalten D.M., Milne K.G., Zou J.Y., Kleywegt G.J., Bergfors T., Ferguson M.A., Knudsen J., Jones T.A. Binding site differences revealed by crystal structures of Plasmodium falciparum and bovine acyl-CoA binding protein. Journal Molecular Biology 2001, 309(1):181-192.
61. Liu J.Y., Jiang T., Zhang J.P., Liang D.C. Crystal structure of allophycocyanin from red algae Porphyra yezoensis at 2.2-A resolution. Journal Biology Chemistry 1999, 274(24):16945-16952.
62. Landan G., Graur D. Characterization of pairwise and multiple sequence alignment errors. Gene 2009, 441(1-2):141-147.