Improving enzyme regulatory protein classification by means of SVM-RFE feature selection

Molecular BioSystems

Volumn 10, Issue 5, 2014, Pages 1063-1071

  Fernandez Lozano, Carlos ^a   Fernández Blanco, Enrique ^a   Dave, Kirtan ^b   Pedreira, Nieves ^a   Gestal, Marcos ^a   Dorado, Julián ^a   Munteanu, Cristian R ^a  

^a UNIVERSITY OF A CORUÑA   (Spain)

^b SARDAR PATEL UNIVERSITY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ENZYME;

ARTICLE; ARTIFICIAL INTELLIGENCE; COMPUTER PROGRAM; METABOLISM; PROBABILITY; PROTEIN DATABASE; RECEIVER OPERATING CHARACTERISTIC; STANDARD; SUPPORT VECTOR MACHINE;

ARTIFICIAL INTELLIGENCE; DATABASES, PROTEIN; ENZYMES; MARKOV CHAINS; REFERENCE STANDARDS; ROC CURVE; SOFTWARE; SUPPORT VECTOR MACHINES;

EID: 84897561080     PISSN: 1742206X     EISSN: 17422051     Source Type: Journal    
DOI: 10.1039/c3mb70489k     Document Type: Article

References (68)

1. A. Smith, S. P. Datta, G. H. Smith, P. N. Campbell, R. Bentley and H. McKenzie, Oxford dictionary of biochemistry and molecular biology, Oxford University Press, 2000
2. C. M. Grisham and R. H. Garrett, Biochemistry, Saunders College Pub., Philadelphia, 1999, pp. 426-427
3. R. Wolfenden M. J. Snider Acc. Chem. Res. 2001 34 938 945
4. K. E. Neet Methods Enzymol. 1995 249 519 567
5. C. R. Munteanu H. Gonzalez-Diaz A. L. Magalhaes J. Theor. Biol. 2008 254 476 482
6. C. R. Munteanu J. M. Vazquez J. Dorado A. P. Sierra A. Sanchez-Gonzalez F. J. Prado-Prado H. Gonzalez-Diaz J. Proteome Res. 2009 8 5219 5228
7. Y. Rodriguez-Soca C. R. Munteanu J. Dorado A. Pazos F. J. Prado-Prado H. Gonzalez-Diaz J. Proteome Res. 2010 9 1182 1190
8. Y. Rodriguez-Soca C. R. Munteanu J. Dorado J. Rabuñal A. Pazos H. González-Díaz Polymer 2010 51 264 273
9. H. Gonzalez-Diaz F. Prado-Prado X. Garcia-Mera N. Alonso P. Abeijon O. Caamano M. Yanez C. R. Munteanu A. Pazos M. A. Dea-Ayuela M. T. Gomez-Munoz M. M. Garijo J. Sansano F. M. Ubeira J. Proteome Res. 2011 10 1698 1718
10. H. Gonzalez-Diaz C. R. Munteanu L. Postelnicu F. Prado-Prado M. Gestal A. Pazos Mol. BioSyst. 2012 8 851 862
11. S. Archer NIDA Res. Monogr. 1978 86 102
12. Recent Advances in QSAR Studies: Methods and Applications, ed., T. Puzyn, J. Leszczynski, and, M. T. D. Cronin, Springer, 2010
13. V. Aparna J. Jeevan M. Ravi G. R. Desiraju B. Gopalakrishnan Bioorg. Med. Chem. Lett. 2006 16 1014 1020
14. A. Speck-Planche and V. V. Kleandrova, Curr. Top. Med. Chem., Netherlands, 2012, vol. 12, pp. 1734-1747
15. A. Speck-Planche, V. V. Kleandrova, F. Luan and M. N. Cordeiro, Bioorg. Med. Chem., Elsevier Ltd, England, 2012, vol. 20, pp. 4848-4855
16. H. Gonzalez-Diaz F. J. Prado-Prado L. Santana E. Uriarte Bioorg. Med. Chem. 2006 14 5973 5980
17. F. J. Prado-Prado I. Garcia X. Garcia-Mera H. Gonzalez-Diaz Chemom. Intell. Lab. Syst. 2011 107 227 233
18. A. R. Katritzky O. V. Kulshyn I. Stoyanova-Slavova D. A. Dobchev M. Kuanar D. C. Fara M. Karelson Bioorg. Med. Chem. 2006 14 2333 2357
19. O. Ivanciuc Curr. Proteomics 2009 6 289 302
20. H. González-Díaz F. Prado-Prado L. G. Pérez-Montoto A. Duardo-Sánchez A. López-Díaz Curr. Proteomics 2009 6 214 227
21. H. Gonzalez-Diaz A. Perez-Bello E. Uriarte Y. Gonzalez-Diaz Bioorg. Med. Chem. Lett. 2006 16 547 553
22. S. Carbon A. Ireland C. J. Mungall S. Shu B. Marshall S. Lewis Bioinformatics 2009 25 288 289
23. C. R. Munteanu and H. González-Díaz, MInD-Prot-Markov Indices for Drugs and Proteins, Register No.: 03/2012/1051 (SC-228-12), (2012), Santiago de Compostela, Spain
24. M. Hall E. Frank G. Holmes B. Pfahringer P. Reutemann I. A. Witten SIGKDD Explorations 2009 11 10 18
25. I. H. W. A. E. Frank, Data Mining Practical Machine Learning Tools and Techniques, Kaufmann, San Francisco, 2nd edn, 2005
26. V. Aguiar-Pulido J. A. Seoane J. R. Rabunal J. Dorado A. Pazos C. R. Munteanu Molecules 2010 15 4875 4889
27. W. S. Hayes M. Borodovsky Genome Res. 1998 8 1154 1171
28. K. Prank E. Schulze O. Eckert T. W. Nattkemper M. Bettendorf C. Maser-Gluth T. J. Sejnowski A. Grote E. Penner A. von Zur Muhlen G. Brabant Eur. J. Endocrinol. 2005 153 301 305
29. B. K. Lavine C. E. Davidson W. S. Rayens Comb. Chem. High Throughput Screening 2004 7 115 131
30. J. Devillers and A. T. Balaban, Topological Indices and Related Descriptors in QSAR and QSPR, Gordon and Breach, The Netherlands, 1999
31. H. M. Berman J. Westbrook Z. Feng G. Gilliland T. N. Bhat H. Weissig I. N. Shindyalov P. E. Bourne Nucleic Acids Res. 2000 28 235 242
32. G. Wang J. R. L. Dunbrack Bioinformatics 2003 19 1589 1591
33. H. Gonzalez-Diaz F. Romaris A. Duardo-Sanchez L. G. Perez-Montoto F. Prado-Prado G. Patlewicz F. M. Ubeira Curr. Pharm. Des. 2010 16 2737 2764
34. H. Gonzalez-Diaz A. Duardo-Sanchez F. M. Ubeira F. Prado-Prado L. G. Perez-Montoto R. Concu G. Podda B. Shen Curr. Drug Metab. 2010 11 379 406
35. H. Gonzalez-Diaz F. Prado-Prado F. M. Ubeira Curr. Top. Med. Chem. 2008 8 1676 1690
36. R. Todeschini and V. Consonni, Handbook of Molecular Descriptors, Wiley-VCH, 2002
37. T. J. Hou X. J. Xu J. Chem. Inf. Comput. Sci. 2003 43 1058 1067
38. H. González-Díaz L. Saiz-Urra R. Molina L. Santana E. Uriarte J. Proteome Res. 2007 6 904 908
39. H. Gonzalez-Diaz L. Saiz-Urra R. Molina Y. Gonzalez-Diaz A. Sanchez-Gonzalez J. Comput. Chem. 2007 28 1042 1048
40. H. Gonzalez-Diaz R. Molina E. Uriarte FEBS Lett. 2005 579 4297 4301
41. R. Concu G. Podda E. Uriarte H. Gonzalez-Diaz J. Comput. Chem. 2009 30 1510 1520
42. H. González-Díaz Y. Pérez-Castillo G. Podda E. Uriarte J. Comput. Chem. 2007 28 1990 1995
43. G. J. McLachlan, K.-A. Do and C. Ambroise, Analyzing microarray gene expression data, Wiley, 2004
44. C. Ferri J. Hernandez-Orallo R. Modroiu Pattern Recognit. Lett. 2009 30 27 38
45. I. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann, 2nd edn, 2005
46. H. Liu and R. Setiono, 13th International Conference on Machine Learning, Bari, Italy, 1996
47. C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, USA, 1995
48. C. M. Bishop, Pattern recognition and machine learning, Springer, 2006
49. G. H. John and P. Langley, 11th Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec, 1995
50. L. Breiman Mach. Learn. 2001 45 5 32
51. J. G. Cleary and L. E. Trigg, Machine Learning International Workshop, 1995
52. D. W. Aha D. Kibler M. K. Albert Mach. Learn. 1991 6 37 66
53. T. Cover P. Hart IEEE Trans. Inf. Theory 1967 13 21 27
54. D. W. Aha Int. J. Man-Mach. Stud. 1992 36 267 287
55. D. W. Aha and D. Kibler, Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, 1989
56. S. Cost S. Salzberg Mach. Learn. 1993 10 57 78
57. C. E. Shannon, W. Weaver, R. E. Blahut and B. Hajek, The mathematical theory of communication, University of Illinois Press, Urbana, 1949
58. D. J. MacKay, Information theory, inference and learning algorithms, Cambridge University Press, 2003
59. C. J. C. Burges Data Min. Knowl. Disc. 1998 2 121 167
60. O. Chapelle P. Haffner V. N. Vapnik IEEE Trans. Neural Networ. 1999 10 1055 1064
61. L. S. Moulin A. P. Alves Da Silva M. A. El-Sharkawi R. J. Marks Ii IEEE Trans. Power Syst. 2004 19 818 825
62. I. Guyon J. Weston S. Barnhill V. Vapnik Mach. Learn. 2002 46 389 422
63. R Core Team, R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, 2013, http://www.R-project.org/
64. M. Kuhn Journal of Statistical Software 2008 28 1 26
65. X. Robin N. Turck A. Hainard N. Tiberti F. Lisacek J.-C. Sanchez M. Müller BMC Bioinf. 2011 12 77
66. F. Fernández-Navarro C. Hervás-Martínez P. Antonio Gutiérrez Pattern Recogn. 2011 44 1821 1833
67. N. V. Chawla K. W. Bowyer L. O. Hall W. P. Kegelmeyer J. Artif. Int. Res. 2002 16 321 357
68. R. Kohavi G. H. John Artif. Intell. 1997 97 273 324