Random Forest classification based on star graph topological indices for antioxidant proteins

Journal of Theoretical Biology

Volumn 317, Issue , 2013, Pages 331-337

  Fernández Blanco, Enrique ^a   Aguiar Pulido, Vanessa ^a   Robert Munteanu, Cristian ^a   Dorado, Julian ^a  

^a UNIVERSITY OF A CORUÑA   (Spain)

Author keywords

Antioxidant protein; Multi target QSAR; Star Graph; Topological indices

Indexed keywords

ANTIOXIDANT PROTEIN; MEMBRANE PROTEIN; UNCLASSIFIED DRUG;

ANTIOXIDANT; FOREST; GRAPHICAL METHOD; PROTEIN; TOPOLOGY;

ACCURACY; ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; COMPUTER GRAPHICS; DATA BASE; MATHEMATICAL ANALYSIS; MATHEMATICAL PARAMETERS; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN STRUCTURE; RANDOM FOREST; SIMULATION; STAR GRAPH;

ALGORITHMS; AMINO ACID SEQUENCE; ANTIOXIDANTS; DATABASES, PROTEIN; MOLECULAR SEQUENCE DATA; PROTEINS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; ROC CURVE;

EID: 84869432205     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2012.10.006     Document Type: Article

References (61)

1. Agüero-Chapin G., Gonzalez-Diaz H., Molina R., Varona-Santos J., Uriarte E., Gonzalez-Diaz Y. Novel 2D maps and coupling numbers for protein sequences. The first QSAR study of polygalacturonases; isolation and prediction of a novel sequence from Psidium guajava L. FEBS Lett. 2006, 580:723-730.
2. Aguiar-Pulido V., Munteanu C.R., Seoane J.A., Fernández-Blanco E., Pérez-Montoto L.G., González-Díaz H., Dorado J. Naïve Bayes QSDR classification based on spiral-graph Shannon entropies for protein biomarkers in human colon cancer. Mol. Biosyst. 2012, 8:1716-1722.
3. Aledo J.C., Li Y., de Magalhães J.P., Ruiz-Camacho M., Perez-Claros J.A. Mitochondrially encoded methionine is inversely related to longevity in mammals. Aging Cell 2011, 10:198-207.
4. Aledo J.C., Valverde H., de Magalhães J.P. Mutational bias plays an important role in shaping longevity-related amino acid content in Mammalian mtDNA-encoded proteins. J. Mol. Evol. 2012, 74:332-341.
5. Althaus I.W., Chou J.J., Gonzales A.J., Diebel M.R., Chou K.C., Kezdy F.J., Romero D.L., Aristoff P.A., Tarpley W.G., Reusser F. Kinetic studies with the nonnucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry 1993, 32:6548-6554.
6. Althaus I.W., Chou J.J., Gonzales A.J., Diebel M.R., Chou K.C., Kezdy F.J., Romero D.L., Aristoff P.A., Tarpley W.G., Reusser F. Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E. J. Biol. Chem. 1993, 268:6119-6124.
7. Althaus I.W., Chou J.J., Gonzales A.J., LeMay R.J., Deibel M.R., Chou K.C., Kezdy F.J., Romero D.L., Thomas R.C., Aristoff P.A., et al. Steady-state kinetic studies with the polysulfonate U-9843, an HIV reverse transcriptase inhibitor. Experientia 1994, 50:23-28.
8. Althaus I.W., Chou K.C., Lemay R.J., Franks K.M., Deibel M.R., Kezdy F.J., Resnick L., Busso M.E., So A.G., Downey K.M., Romero D.L., Thomas R.C., Aristoff P.A., Tarpley W.G., Reusser F. The benzylthio-pyrimidine U-31,355, a potent inhibitor of HIV-1 reverse transcriptase. Biochem. Pharmacol. 1996, 51:743-750.
9. Berman H.M., Westbrook J., Feng Z., Gilliland G., Bhat T.N., Weissig H., Shindyalov I.N., Bourne P.E. The Protein Data Bank. Nucleic Acids Res. 2000, 28:235-242.
10. Bielińska-Wa-z D., Nowak W., Wa-z P., Nandyc A., Clark T. Distributionmoments of 2D-graphs as descriptors of DNAsequences. Chem. Phys. Lett. 2007, 443:408-413.
11. Breiman L. Bagging predictors. Mach. Learn. 1996, 24:123-140.
12. Breiman L. Random Forest. Mach. Learn. 2001, 45:5-32.
13. Cevenini E., Bellavista E., Tieri P., Castellani G., Lescai F., Francesconi M., Mishto M., Santoro A., Valensin S., Salvioli S., Capri M., Zaikin A., Monti D., de Magalhães J.P., Franceschi C. Systems biology and longevity: an emerging approach to identify innovative anti-aging targets and strategies. Curr. Pharm. Des. 2010, 16:802-813.
14. Chipman H.A., George E.I., McCulloch R.E. An introduction to Classification and Regression Tree (CART) analysis. J. Am. Stat. Assoc. 1998, 935-948.
15. Chou K.C. Graphical rules in steady and non-steady enzyme kinetics. J. Biol. Chem. 1989, 264:12074-12079.
16. Chou K.C. Review: applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady state systems. Biophys. Chem. 1990, 35:1-24.
17. Chou K.C., Forsen S. Graphical rules for enzyme-catalyzed rate laws. Biochem. J. 1980, 187:829-835.
18. Chou K.C., Kezdy F.J., Reusser F. Review: steady-state inhibition kinetics of processive nucleic acid polymerases and nucleases. Anal. Biochem. 1994, 221:217-230.
19. Chou K.C., Liu W.M. Graphical rules for non-steady state enzyme kinetics. J. Theor. Biol. 1981, 91:637-654.
20. Chou K.C., Zhang C.T. Diagrammatization of codon usage in 339 HIV proteins and its biological implication. AIDS Res. Hum. Retroviruses 1992, 8:1967-1976.
21. Chou K.C., Zhang C.T., Elrod D.W. Do "antisense proteins" exist?. J. Protein Chem. 1996, 15:59-61.
22. de Magalhães J.P., Curado J., Church G.M. Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics 2009, 25:875-881.
23. de Magalhães J.P., Finch C.E., Janssens G. Next-generation sequencing in aging research: emerging applications, problems, pitfalls and possible solutions. Ageing Res. Rev. 2010, 9:315-323.
24. de Magalhães J.P. The biology of ageing: a primer. An Introduction to Gerontology 2011, 21-47. Cambridge University Press, Cambridge, UK. S.-H. I. (Ed.).
25. de Magalhães J.P., Wuttke D., Wood S.H., Plank M., Vora C. Genome-environment interactions that modulate aging: powerful targets for drug discovery. Pharmacol. Rev. 2012, 64:88-101.
26. Devillers J., Balaban A.T. Topological Indices and Related Descriptors in QSAR and QSPR 1999, Gordon and Breach, The Netherlands.
27. Diao Y., Li M., Feng Z., Yin J., Pan Y. The community structure of human cellular signaling network. J. Theor. Biol. 2007, 247:608-615.
28. Freitas A.A., de Magalhães J.P. A review and appraisal of the DNA damage theory of ageing. Mutat. Res. 2012, 728:12-22.
29. Freitas A.A., Vasieva O., de Magalhães J.P. A data mining approach for classifying DNA repair genes into ageing-related or non-ageing-related. BMC Genomics. 2011, 12:27.
30. Gomes N.M., Ryder O.A., Houck M.L., Charter S.J., Walker W., Forsyth N.R., Austad S.N., Venditti C., Pagel M., Shay J.W., Wright W.E. Comparative biology of mammalian telomeres: hypotheses on ancestral states and the roles of telomeres in longevity determination. Aging Cell 2011, 10:761-768.
31. González-Díaz H., Bonet I., Terán C., de Clercq E., Bello R., García M., Santana L., Uriarte E. ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds. Eur. J. Med. Chem. 2007, 42:580-585.
32. Gonzalez-Diaz H., Gonzalez-Diaz Y., Santana L., Ubeira F.M., Uriarte E. Proteomics, networks and connectivity indices. Proteomics 2008, 8:750-778.
33. González-Díaz H., Sanchez-Gonzalez A., Gonzalez-Diaz Y. 3D -QSAR study for DNA cleavage proteins with a potential anti-tumor ATCUN-like motif. J. Inorg. Biochem. 2006, 100:1290-1297.
34. González-Díaz H., Vilar S., Rivero D., Fernández-Blanco E., Porto A., Munteanu C.R. QSPR Models for Cerebral Cortex Co-Activation Networks, Topological Indices for Medicinal Chemistry, Biology, Parasitology, and Social Networks 2010, Research Signpost.
35. González-Díaz H., Vilar S., Santana L., Uriarte E. Medicinal chemistry and bioinformatics-current trends in drugs discovery with networks topological indices. Curr. Top Med. Chem. 2007, 7:1025-1039.
36. Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I.A. The WEKA data mining software: an update. SIGKDD Explor. 2009, 11.
37. Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I.H. The WEKA data mining software: an update. SIGKDD Explor. 2009, 11.
38. Harary F. 1969, Graph Theory, Reading, MA.
39. Harman D. The aging process. Proc. Natl. Acad. Sci. U.S.A. 1981, 78:7124-7128.
40. Hayflick L. The future of ageing. Nature 2000, 408:267-269.
41. Koutsofios E., North S.C. Drawing Graphs with Dot 1993, AT&T Bell Laboratories, Murray Hill, NJ, USA.
42. Kuzmic P., Ng K.Y., Heath T.D. Mixtures of tight-binding enzyme inhibitors. Kinetic analysis by a recursive rate equation. Anal. Biochem. 1992, 200:68-73.
43. Li Y.H., Dong M.Q., Guo Z. Systematic analysis and prediction of longevity genes in Caenorhabditis elegans. Mech. Ageing Dev. 2010, 131:700-709.
44. McLachlan G.J., Do K.-A., Ambroise C. Analyzing Microarray Gene Expression Data 2004, Wiley.
45. Munteanu C.R., Fernandez-Blanco E., Seoane J.A., Izquierdo-Novo P., Rodriguez-Fernandez J.A., Prieto-Gonzalez J.M., Rabunal J.R., Pazos A. Drug discovery and design for complex diseases through QSAR computational methods. Curr. Pharm. Design 2010, 16:2640-2655.
46. Munteanu C.R., Magalhães A.L., Uriarte E., González-Díaz H. Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices. J. Theor. Biol. 2009, 257:303-311.
47. OECD, 2011. http://stats.oecd.org/index.aspx?DataSetCode=HEALTH_STAT.
48. Prado-Prado F.J., González-Díaz H., Martinez de la Vega O., Ubeira F.M., Chou K.C. Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorg. Med. Chem. 2008, 16:5871-5880.
49. Qi X.Q., Wen J., Qi Z.H. New 3D graphical representation of DNA sequence based on dual nucleotides. J. Theor. Biol. 2007, 249:681-690.
50. Randić M., Balaban A.T. On a four-dimensional representation of DNA primary sequences. J. Chem. Inf. Model. 2003, 43:532-539.
51. Randić M., Zupan J., Vikic-Topic D. On representation of proteins by star-like graphs. J. Mol. Graph. Model 2007, 290-305.
52. Rappin N., Dunn R. wxPython in Action 2006, Manning Publications Co., Greenwich, CT.
53. Regöly-Mérei A., Bereczky M., Arató G., Telek G., Pallai Z., Lugasi A., Antal M. Nutritional and antioxidant status of colorectal cancer patients. Orv. Hetil. 2007, 148:1505-1509.
54. Riera-Fernández I., Martín-Romalde R., Prado-Prado F., Escobar M., Munteanu C., Concu R., Duardo-Sanchez A., González-Díaz H. From QSAR models of drugs to complex networks: state-of-art review and introduction of new Markov-spectral moments indices. Curr. Top. Med. Chem. 2012, 8:927-960.
55. Rivero, D., Fernandez-Blanco, E., Dorado, J., Pazos, A., 2011. Using recurrent ANNs for the detection of epileptic seizures in EEG signals. Evolutionary Computation (CEC), 2011 IEEE Congress on IEEE, pp. 587-592.
56. Shindyalov I.N., Bourne P.E. Protein structure alignment by incremental combinatorial extension of the optimum path. Protein Eng. 1998, 11:739-747.
57. Skurichina M., Duin R.P.W. Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal. Appl. 2002, 5:121-135.
58. Todeschini R., Consonni V. Handbook of Molecular Descriptors 2002, Wiley-VCH.
59. Vapnik, V.N., 1995. The Nature of Statistical Learning Theory.
60. Wang G., Dunbrack R.L. PISCES: a protein sequence culling server. Bioinformatics 2003, 19:1589-1591.
61. Zhang C.T., Chou K.C. Analysis of codon usage in 1562 E. coli protein coding sequences. J. Mol. Biol. 1994, 238:1-8.