Graph-based processing of macromolecular information

Current Bioinformatics

Volumn 10, Issue 5, 2015, Pages 606-631

  Munteanu, Cristian R ^a,b   Aguiar Pulido, Vanessa ^a   Freire, Ana ^c   Martínez Romero, Marcos ^a   Porto Pazos, Ana B ^a   Pereira, Javier ^a   Dorado, Julian ^a  

^a UNIVERSITY OF A CORUÑA   (Spain)

^b MAASTRICHT UNIVERSITY   (Netherlands)

^c UNIVERSITAT POMPEU FABRA   (Spain)

Author keywords

Complex networks; Graphs; Markov descriptors; Molecular information; Protein topological indices; QSAR

Indexed keywords

BIOACTIVITY; COMPLEX NETWORKS; COMPUTATIONAL CHEMISTRY; DIAGNOSIS; GRAPH THEORY; GRAPHIC METHODS; MOLECULAR BIOLOGY; NUCLEIC ACIDS;

COMPLEX INFORMATION; DESCRIPTORS; ELEMENT CONNECTIVITY; GRAPH; GRAPH-BASED; MARKOV DESCRIPTOR; MOLECULAR INFORMATION; PROTEIN TOPOLOGICAL INDEX; QSAR; TOPOLOGICAL INDEX;

PROTEINS;

DRUG; NUCLEIC ACID; PROTEIN;

ARTICLE; CALCULATION; COMPUTER PROGRAM; INFORMATION PROCESSING; MACROMOLECULE; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL;

EID: 84959562437     PISSN: 15748936     EISSN: 2212392X     Source Type: Journal    
DOI: 10.2174/1574893610666151008012438     Document Type: Article

References (133)

1. Cepeda MS, Lobanov V, Berlin JA. From ClinicalTrials.gov trial registry to an analysis-ready database of clinical trial results. Clin Trials 2013; 10(2): 347-8.
2. Harary F. Graph Theory. Westview Press: MA 1969.
3. Weininger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Model 1988; 28(1): 31-6.
4. Guha R, Howard MT, Hutchison GR, Murray-Rust P, Rzepa H, Steinbeck C, Wegner J, Willighagen EL. The Blue Obeliskinteroperability in chemical informatics. J Chem Inf Model 2006; 46(3): 991-8.
5. Balaban AT. Graph Theory and Topology in Chemistry. Elsevier: Amsterdam 1987.
6. Bonchev D, Rouvray DH. Chemical Graph Theory. Gordon & Breach: NewYork 1991.
7. Mason O, Verwoerd M. Graph theory and networks in Biology. IET Syst Biol 2007; 1(2): 89-119.
8. Lazer D, Pentland A, Adamic L, Aral S, Barabasi AL, Brewer D, Christakis N, Contractor N, Fowler J, Gutmann M, Jebara T, King G, Macy M, Roy D, Van Alstyne M. Social science. Computational social science. Science 2009; 323(5915): 721-3.
9. Jones JJ, Settle JE, Bond RM, Fariss CJ, Marlow C, Fowler JH. Inferring tie strength from online directed behavior. PLoS ONE 2013; 8(1): e52168.
10. Bolte J, Kerner J. Quantum graphs with two-particle contact interactions. J Phys A: Math Theor 2013; 46: 045207.
11. Balaban AT. Chemical graphs. XXXIV. Five new topological indices for the branching of tree-like graphs. Theor Chim Acta 1979; 53: 355-75.
12. Randic M, Zupan J, Vikic-Topic D. On representation of proteins by star-like graphs. J Mol Graph Model 2007: 290-305.
13. Yook SH, Oltvai ZN, Barabasi AL. Functional and topological characterization of protein interaction networks. Proteomics 2004; 4(4): 928-42.
14. Nilsson D, Andersson B. A graphical tool for parasite genome annotation. Comput Methods Programs Biomed 2004; 73(1): 55-60.
15. Concu R, Podda G, Ubeira FM, Gonzalez-Diaz H. Review of QSAR Models for Enzyme Classes of Drug Targets: Theoretical Background and Applications in Parasites, Hosts, and other Organisms. Curr Pharm Des 2010; 16(24): 2710-23.
16. Altmann M. Reinterpreting network measures for models of disease transmission. Soc Networks 1993; 15(1): 1-17.
17. Cruz-Monteagudo M, Munteanu CR, Borges F, Cordeiro MN, Uriarte E, Chou KC, González-Díaz H. Stochastic molecular descriptors for polymers. 4. Study of complex mixtures with topological indices of mass spectra spiral and star networks: The blood proteome case. Polymer 2008; 49: 5575-87.
18. Read RC, Wilson RJ. An Atlas of Graphs. Clarendon Press: 1998.
19. Balaban AT. Topological indices based on topological distances in molecular graphs. Pure Appl Chem 1983; 55(2): 199-206.
20. Bharucha-Reid AT. McGraw-Hill Series in Probability an Statistic. New York, McGraw-Hill Book Company 1960;167-434.
21. Barigye SJ, Marrero-Ponce Y, Santiago OM, Lopez YM, Perez- Gimenez F, Torrens F. Shannon's, mutual, conditional and joint entropy information indices: generalization of global indices defined from local vertex invariants. Curr Comput Aided Drug Des 2013; 9(2): 164-83.
22. Todeschini R, Consonni V. Handbook of Molecular Descriptors. Wiley-VCH: 2002.
23. Hahn HK, Schoenberger K. The ordered distribution of natural numbers on the square root spiral. 2007.
24. Gonzalez-Diaz H, Perez-Montoto LG, Duardo-Sanchez A, Paniagua E, Vazquez-Prieto S, Vilas R, Dea-Ayuela MA, Bolas- Fernandez F, Munteanu CR, Dorado J, Costas J, Ubeira FM. Generalized lattice graphs for 2D-visualization of biological information. J Theor Biol 2009; 261(1): 136-47.
25. Randic M, Balaban AT. On a four-dimensional representation of DNA primary sequences. J Chem Inf Comput Sci 2003; 43(2): 532-9.
26. Randic M. A Graph Theoretical Characterization of Proteomics Maps. Int J Quant Chem 2002 90: 848-58.
27. Randic M. Graphical representations of DNA as 2-D map. Chem Phys Lett 2004; 386(4): 468-71.
28. Berger B, Leighton T. Protein folding in the hydrophobichydrophilic (HP) model is NP-complete. J Comput Biol 1998; 5(1): 27-40.
29. Gates MA. A simple way to look at DNA. J Theor Biol 1986; 119: 319-28.
30. Randić M, Guo X, Basak SC. On the characterization of DNA primary sequences by triplet of nucleic acid bases. J Chem Inf Comput Sci 2001; 41(3): 619-26.
31. Nandy A. Novel Method for Discrimination of Conserved Genes through Numerical Characterization of DNA Sequences. Int E J Mol Design 2003; 2: 000-000.
32. Roy A, Raychaudhur C, Nandy A. Novel techniques of graphical representation and analysis of DNA sequences-A review. J Biosci 1998; 23(1): 55-71.
33. Nandy A. Two-dimensional graphical representation of DNA sequences and intron-exon discrimination in intron-rich sequences. CABIOS (Comput-Appl-Biosci) 1996; 12(1): 55-62.
34. Aguero-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 letters 2006; 580(3): 723-30.
35. González-Díaz H, Pérez-Bello A, Cruz-Monteagudo M, González- Díaz Y, Santana L, Uriarte E. Chemometrics for QSAR with low sequence homology: Mycobacterial promoter sequences recognition with 2D-RNA entropies. Chemom Intell Lab Systs 2007; 85: 20-6.
36. Yuan Z. Prediction of protein subcellular locations using Markov chain models. FEBS letters 1999; 451(1): 23-6.
37. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The Protein Data Bank Nucleic Acids Res 2000; 28: 235-42.
38. Munteanu CR, Gonzáles-Díaz H.: S2SNet - Sequence to Star Network, Reg. No. 03/2008/1338, Santiago de Compostela, Spain. Santiago de Compostela, Spain 2008.
39. Pérez Montoto LG, Prado-Prado FJ, Munteanu CR, Gonzalez Diaz H.: CULSPIN - Compute ULam SPiral INdices, Register No.: 03/2009/1199 (SC-207-09), Spain. Santiago de Compostela 2009.
40. Munteanu CR, González-Díaz H.: MInD-Prot - Markov Indices for Drugs and Proteins, Register No.: 03/2012/1051 (SC-228-12). Santiago de Compostela, Spain 2012.
41. Steinbeck C, Han Y, Kuhn S, Horlacher O, Luttmann E, Willighagen E. The Chemistry Development Kit (CDK): an opensource Java library for Chemo- and Bioinformatics. J Chem Inf Comput Sci 2003; 43(2): 493-500.
42. Guha R. Chemical Informatics Functionality in R. J Stat Software 2007; 18: 1-16.
43. Spjuth O, Helmus T, Willighagen EL, et al. Bioclipse: an open source workbench for chemo- and bioinformatics. BMC Bioinformatics 2007; 8: 59.
44. Beisken S, Meinl T, Wiswedel B, de Figueiredo LF, Berthold M, Steinbeck C.: KNIME-CDK: Workflow-driven cheminformatics. BMC Bioinformatics 2013; 14: 257.
45. Lawson KR, Lawson J. LICSS - a chemical spreadsheet in microsoft excel. J Cheminform 2012; 4(1): 3.
46. Mauri A, Consonni V, Pavan M, Todeschini R. DRAGON Software: An Easy Approach to Molecular Descriptor Calculations. MATCH, communications in mathematical and in computer chemistry 2006; 56: 237-48.
47. Tetko IV, Gasteiger J, Todeschini R, et al. Virtual computational chemistry laboratory - design and description. J Comput Aided Mol Des 2005; 19: 453-63.
48. Ponce YM. Total and local (atom and atom type) molecular quadratic indices: significance interpretation, comparison to other molecular descriptors, and QSPR/QSAR applications. Bioorg Med Chem 2004; 12(24): 6351-69.
49. Casanola-Martin GM, Marrero-Ponce Y, Khan MT, Ather A, Khan KM, Torrens F, Rotondo R. Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays. Eur J Med Chem 2007; 42(11-12): 1370-81.
50. Perez-Garrido A, Helguera AM, Rodriguez FG, Cordeiro MN. QSAR models to predict mutagenicity of acrylates, methacrylates and alpha,beta-unsaturated carbonyl compounds. Dent Mater 26(5): 397-415.
51. Estrada E, Quincoces JA, Patlewicz G. Creating molecular diversity from antioxidants in Brazilian propolis. Combination of TOPS-MODE QSAR and virtual structure generation. Mol Divers 2004; 8(1): 21-33.
52. Cabrera-Pérez MA, Bermejo-Sanz M, Ramos-Torres L, Grau-Ávalos R, Pérez-González M, González-Díaz H. A topological substructural approach for predicting human intestinal absorption of drugs. Eur J Med Chem 2004; 39: 905-16.
53. Molina-Ruiz R, Saiz-Urra L, Rodriguez-Borges JE, et al. A TOPological Sub-structural Molecular Design (TOPS-MODE)- QSAR approach for modeling the antiproliferative activity against murine leukemia tumor cell line (L1210). Bioorg Med Chem 2009; 17(2): 537-47.
54. Casanola-Martin GM, Marrero-Ponce Y, Tareq Hassan Khan M, Torrens F, Perez-Gimenez F, Rescigno A. Atom- and bond-based 2D TOMOCOMD-CARDD approach and ligand-based virtual screening for the drug discovery of new tyrosinase inhibitors. J Biomol Screen 2008; 13(10): 1014-24.
55. Gonzalez-Diaz H, Romaris F, Duardo-Sanchez A, Perez-Montoto LG, Prado-Prado F, Patlewicz G, Ubeira FM. Predicting drugs and proteins in parasite infections with topological indices of complex networks: theoretical backgrounds, applications, and legal issues. Curr Pharm Des 2010; 16(24): 2737-64.
56. Casanola-Martin GM, Khan MT, Marrero-Ponce Y, Ather A, Sultankhodzhaev MN, Torrens F. New tyrosinase inhibitors selected by atomic linear indices-based classification models. Bioorg Med Chem Lett 2006; 16(2): 324-30.
57. Castillo-Garit JA, Marrero-Ponce Y, Escobar J, Torrens F, Rotondo R. A novel approach to predict aquatic toxicity from molecular structure. Chemosphere 2008; 73(3): 415-27.
58. Castillo-Garit JA, Marrero-Ponce Y, Torrens F, Garcia-Domenech R. Estimation of ADME properties in drug discovery: predicting Caco-2 cell permeability using atom-based stochastic and nonstochastic linear indices. J Pharm Sci 2008; 97(5): 1946-76.
59. Ponce YM, Marrero RM, Castro EA, Ramos de Armas R, Diaz HG, Zaldivar VR, Torrens F. Protein quadratic indices of the "macromolecular pseudograph's alpha-carbon atom adjacency matrix". 1. Prediction of Arc repressor alanine-mutant's stability. Molecules 2004; 9(12): 1124-47.
60. Marrero-Ponce Y, Medina-Marrero R, Castro AE, Ramos de Armas R, González-Díaz H, Romero-Zaldivar V, Torrens F. Protein Quadratic Indices of the “Macromolecular Pseudograph’s α-Carbon Atom Adjacency Matrix”. 1. Prediction of Arc Repressor Alanine-mutant’s Stability. Molecules 2004; 9: 1124-47.
61. Marrero-Ponce Y, Nodarse D, González-Díaz H, Ramos de Armas R, Romero-Zaldivar V, Torrens F, Castro EA. Nucleic Acid Quadratic Indices of the “Macromolecular Graph’s Nucleotides Adjacency Matrix”. Modeling of Footprints after the Interaction of Paromomycin with the HIV-1 Ψ-RNA Packaging Region. Int J Mol Sci 2004; 5: 276-93.
62. Kier LB, Hall LH. Molecular Structure Description: The Electrotopological State. Academic Press: 1999.
63. Katritzky AR, Oliferenko A, Lomaka A, Karelson M. Sixmembered cyclic ureas as HIV-1 protease inhibitors: a QSAR study based on CODESSA PRO approach. Quantitative structure-activity relationships. Bioorg Med Chem Lett 2002; 12(23): 3453-7.
64. Katritzky AR, Kulshyn OV, Stoyanova-Slavova I, Dobchev DA, Kuanar M, Fara DC, Karelson M. Antimalarial activity: a QSAR modeling using CODESSA PRO software. Bioorg Med Chem 2006; 14(7): 2333-57.
65. Katritzky AR, Dobchev DA, Tulp I, Karelson M, Carlson DA. QSAR study of mosquito repellents using Codessa Pro. Bioorg Med Chem Lett 2006; 16(8): 2306-11.
66. Katritzky AR, Kulshyn OV, Stoyanova-Slavova I, Dobchev DA, Kuanar M, Fara DC, Karelson M. Antimalarial activity: a QSAR modeling using CODESSA PRO software. Bioorganic & medicinal chemistry 2006; 14(7): 2333-57.
67. Rappin N, Dunn R. wxPython in Action. Manning Publications Co.: Greenwich, CT 2006.
68. Gonzalez-Diaz H, Molina-Ruiz R, Hernandez I.: MARCH-INSIDE version 3.0 (MARkov CHains INvariants for SImulation & DEsign); Windows supported version under request to the main author contact email: gonzalezdiazh@yahoo.es. 3.0 ed 2007.
69. Koschützki D. CentiBiN Version 1.4.2. 2006:CentiBiN Version 1.4.2, Centralities in Biological Networks c 2004-6 Dirk Koschützki Research Group Network Analysis, IPK Gatersleben, Germany.
70. Batagelj V, Mrvar A. Pajek, Program for Large Network Analysis (ver. 1.15), http://vlado.fmf.uni-lj.si/pub/networks/pajek/. 1.15 ed 2006.
71. Gonzalez-Diaz H, Duardo-Sanchez A, Ubeira FM, et al. Review of MARCH-INSIDE & complex networks prediction of drugs: ADMET, anti-parasite activity, metabolizing enzymes and cardiotoxicity proteome biomarkers. Curr Drug Metab 2010; 11(4): 379-406.
72. Gonzalez-Diaz H, Prado-Prado F, Ubeira FM. Predicting antimicrobial drugs and targets with the MARCH-INSIDE approach. Curr Top Med Chem 2008; 8(18): 1676-90.
73. Hou TJ, Xu XJ.: ADME evaluation in drug discovery. 2. Prediction of partition coefficient by atom-additive approach based on atomweighted solvent accessible surface areas. J Chem Inf Comput Sci 2003; 43(3): 1058-67.
74. Gonzalez-Diaz H, Saiz-Urra L, Molina R, Santana L, Uriarte E. A Model for the Recognition of Protein Kinases Based on the Entropy of 3D van der Waals Interactions. J Proteome Res 2007; 6(2): 904-8.
75. Gonzalez-Diaz H, Saiz-Urra L, Molina R, Gonzalez-Diaz Y, Sanchez-Gonzalez A. Computational chemistry approach to protein kinase recognition using 3D stochastic van der Waals spectral moments. J Comput Chem 2007; 28(6): 1042-8.
76. Gonzalez-Diaz H, Molina R, Uriarte E. Recognition of stable protein mutants with 3D stochastic average electrostatic potentials. FEBS Lett 2005; 579(20): 4297-301.
77. Concu R, Podda G, Uriarte E, Gonzalez-Diaz H. Computational chemistry study of 3D-structure-function relationships for enzymes based on Markov models for protein electrostatic, HINT, and van der Waals potentials. J Comput Chem 2009; 30: 1510-20.
78. González-Díaz H, Pérez-Castillo Y, Podda G, Uriarte E. Computational Chemistry Comparison of Stable/Nonstable Protein Mutants Classification Models Based on 3D and Topological Indices. J Comput Chem 2007; 28: 1990-5.
79. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IA. The WEKA Data Mining Software: An Update. SIGKDD Explorations 2009; 11(1).
80. Overington J. ChEMBL. An interview with John Overington, team leader, chemogenomics at the European Bioinformatics Institute Outstation of the European Molecular Biology Laboratory (EMBLEBI). Interview by Wendy A. Warr. J Comput Aided Mol Des 2009; 23(4): 195-8.
81. Gaulton A, Bellis LJ, Bento AP, Chambers J, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 2011; 40(Database issue): D1100-7.
82. O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform 2011; 3: 33.
83. Barigye SJ, Marrero-Ponce Y, Perez-Gimenez F, Bonchev D. Trends in information theory-based chemical structure codification. Mol Divers 2013.
84. Todeschini R, Consonni V. Molecular Descriptors for Chemoinformatics. Wiley-VCH: Weinheim 2009.
85. Barigye SJ, Marrero-Ponce Y, Martinez Lopez Y, et al. Eventbased criteria in GT-STAF information indices: theory, exploratory diversity analysis and QSPR applications. SAR QSAR Environ Res 2013; 24(1): 3-34.
86. Marrero-Ponce Y, Santiago OM, Lopez YM, Barigye SJ, Torrens F. Derivatives in discrete mathematics: a novel graph-theoretical invariant for generating new 2/3D molecular descriptors. I. Theory and QSPR application. J Comput Aided Mol Des 2012; 26(11): 1229-46.
87. Martínez-Santiago O, Millán-Cabrera R, Marrero-Ponce Y, et al. Discrete Derivatives for Atom-Pairs as a Novel Graph-Theoretical Invariant for Generating New Molecular Descriptors: Orthogonality, Interpretation and QSARs/QSPRs on Benchmark Databases. Molecular Informatics 2014; 33(5): 343-68.
88. Munteanu CR, Fernandez-Blanco E, Seoane JA, et al. Drug Discovery and Design for Complex Diseases through QSAR Computational Methods. Curr Pharm Des 2010; 16(24): 2640-55.
89. Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MN. Rational drug design for anti-cancer chemotherapy: multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents. Bioorg Med Chem 2012; 20(15): 4848-55.
90. Verma RP, Hansch C. QSAR modeling of taxane analogues against colon cancer. Eur J Med Chem 2010; 45(4): 1470-7.
91. Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MN. Chemoinformatics in anti-cancer chemotherapy: multi-target QSAR model for the in silico discovery of anti-breast cancer agents. Eur J Pharm Sci 2012; 47(1): 273-9.
92. Prado-Prado F, Garcia-Mera X. QSAR models for computer-aided drug design and molecular docking for disorders of the central nervous system and other diseases. Curr Top Med Chem 2012; 12(16): 1731-3.
93. Luan F, Borges F, Cordeiro MN. Recent advances on A(3) adenosine receptor antagonists by QSAR tools. Curr Top Med Chem 2012; 12(8): 878-94.
94. Speck-Planche A, Kleandrova VV, Rojas-Vargas JA. QSAR model toward the rational design of new agrochemical fungicides with a defined resistance risk using substructural descriptors. Mol Diversity 2011; 15(4): 901-9.
95. Prado-Prado FJ, Garcia I, Garcia-Mera X, Gonzalez-Diaz H.: Entropy multi-target QSAR model for prediction of antiviral drug complex networks. Chemom Intell Lab Syst 2011; 107(2): 227-33.
96. Marzaro G, Chilin A, Guiotto A, Uriarte E, Brun P, Castagliuolo I, Tonus F, Gonzalez-Diaz H. Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors. Eur J Med Chem 2011; 46(6): 2185-92.
97. Jeong JA, Cho H, Jung SY, Kang HB, Park JY, Kim J, Choo D, J., Lee JY. 3D QSAR studies on 3,4-dihydroquinazolines as T-type calcium channel blocker by comparative molecular similarity indices analysis (CoMSIA). Bioorg Med Chem Lett 2010; 20(1): 38-41.
98. McLachlan GJ, Do K-A, Ambroise C. Analyzing microarray gene expression data. Wiley: 2004.
99. Ferri C, Hernandez-Orallo J, Modroiu R. An experimental comparison of performance measures for classification. Pattern Recogn Lett 2009; 30(1): 27-38.
100. Witten I, Frank E. Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems). Morgan Kaufmann: 2005.
101. Swets JA. Signal detection theory and ROC analysis in psychology and diagnostics: collected papers. Lawrence Erlbaum Associates: Mahwah, NJ 1996.
102. Teetor P. R Cookbook. O'Reilly: 2011.
103. StatSoft.Inc. STATISTICA, (data analysis software system), version 6.0, www.statsoft.com. 6.0 ed 2002.
104. Caballero J, Fernandez M.: Artificial neural networks from MATLAB in medicinal chemistry. Bayesian-regularized genetic neural networks (BRGNN): application to the prediction of the antagonistic activity against human platelet thrombin receptor (PAR-1). Curr Top Med Chem 2008; 8(18): 1580-605.
105. Liu H, Setiono R. A probabilistic approach to feature selection - A filter solution. 13th International Conference on Machine Learning; 1996; Bari, Italy. Year; pp. 319-27.
106. Bishop CM. Neural Networks for Pattern Recognition. Oxford University Press, USA: 1995.
107. Bishop CM. Pattern recognition and machine learning. Springer: 2006.
108. John GH, Langley P. Estimating Continuous Distributions in Bayesian Classifiers. 11th Conference on Uncertainty in Artificial Intelligence; 1995 August 18-20, 1995; Montreal, Quebec. City: Morgan Kaufman Year; pp. 338-45.
109. Breiman L. Random Forest. Machine Learning 2001; 45: 5-32.
110. Fan R-E, Chang K-W, Hsieh C-J, Wang X-R, Lin C-J.: LIBLINEAR: A Library for Large Linear Classification. J Mach Learn Res 2008; 9: 1871-4.
111. Vapnik VN. Statistical learning theory. Wiley: New York; Chichester 1998.
112. González-Díaz H, Ferino G, Munteanu CR, Vilar S, Uriarte E. In: Oncoproteomics. Cho WK, Ed. Springer 2009.
113. Vázquez JM, Aguiar V, Seoane JA, et al. Star graphs of protein sequences and proteome mass spectra in cancer prediction. Curr Proteomics 2009; 6(4): 275-88.
114. González-Díaz H, Ferino G, Prado-Prado FJ, Vilar S, Uriarte E, Pazos A, Munteanu CR. In: An Omics Perspective on Cancer Research. Cho WCS, Ed. Springer 2010.
115. Martínez-Romero M, Vázquez-Naya JM, Rabuñal JR, et al. Artificial intelligence techniques for colorectal cancer drug metabolism: ontologies and complex networks. Curr Drug Metab 2010; 11(4): 347-68.
116. Munteanu CR, Magalhaes AL, Uriarte E, Gonzalez-Diaz H. Multitarget QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices. J Theor Biol 2009; 257(2): 303-11.
117. Munteanu CR, Gonzalez-Diaz H, Magalhaes AL. Enzymes/nonenzymes classification model complexity based on composition, sequence, 3D and topological indices. J Theor Biol 2008; 254(2): 476-82.
118. Munteanu CR, Gonzalez-Diaz H, Borges F, de Magalhaes AL. Natural/random protein classification models based on star network topological indices. J Theor Biol 2008; 254(4): 775-83.
119. Wang GRL, Dunbrack J. PISCES: a protein sequence culling server. Bioinformatics (Oxford, England) 2003; 19: 1589-91.
120. Van Waterbeemd H. In: Chemometric methods in molecular design. Van Waterbeemd H, Ed. New York, Wiley-VCH 1995; 265-82.
121. Aguiar-Pulido V, Munteanu CR, Seoane JA, Fernandez-Blanco E, Perez-Montoto LG, Gonzalez-Diaz H, Dorado J.: Naive Bayes QSDR classification based on spiral-graph Shannon entropies for protein biomarkers in human colon cancer. Mol BioSyst 2012; 8(6): 1716-22.
122. Cruz-Monteagudo M, Munteanu CR, Borges F, Cordeiro MN, Uriarte E, Gonzalez-Diaz H. Quantitative Proteome-Property Relationships (QPPRs). Part 1: finding biomarkers of organic drugs with mean Markov connectivity indices of spiral networks of blood mass spectra. Bioorg Med Chem 2008; 16(22): 9684-93.
123. González-Díaz H, Torres-Gomez LA, Guevara Y, Almeida MS, Molina R, Castanedo N, Santana L, Uriarte E. Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer-aided molecular design III: 2.5D indices for the discovery of antibacterials. J Mol Model 2005; 11(2): 116-23.
124. Cruz-Monteagudo M, Gonzalez-Diaz H, Borges F, Dominguez ER, Cordeiro MN. 3D-MEDNEs: an alternative "in silico" technique for chemical research in toxicology. 2. quantitative proteome-toxicity relationships (QPTR) based on mass spectrum spiral entropy. Chem Res Toxicol 2008; 21(3): 619-32.
125. González-Díaz H, Marrero Y, Hernandez I, Bastida I, Tenorio E, Nasco O, Uriarte E, Castanedo N, Cabrera MA, Aguila E, Marrero O, Morales A, Perez M. 3D-MEDNEs: an alternative "in silico" technique for chemical research in toxicology. 1. prediction of chemically induced agranulocytosis. Chem Res Toxicol 2003; 16(10): 1318-27.
126. Perez-Bello A, Munteanu CR, Ubeira FM, De Magalhaes AL, Uriarte E, Gonzalez-Diaz H. Alignment-free prediction of mycobacterial DNA promoters based on pseudo-folding lattice network or star-graph topological indices. J Theor Biol 2009; 256(3): 458-66.
127. Vilar S, Gonzalez-Diaz H, Santana L, Uriarte E. QSAR model for alignment-free prediction of human breast cancer biomarkers based on electrostatic potentials of protein pseudofolding HP-lattice networks. J Comput Chem 2008; 29(16): 2613-22.
128. Sjoblom T, Jones S, Wood LD, Parsons DW, Lin J, Barber TD, Mandelker D, Leary RJ, Ptak J, Silliman N, Szabo S, Buckhaults P, Farrell C, Meeh P, Markowitz SD, Willis J, Dawson D, Willson JK, Gazdar AF, Hartigan J, Wu L, Liu C, Parmigiani G, Park BH, Bachman KE, Papadopoulos N, Vogelstein B, Kinzler KW, Velculescu VE. The consensus coding sequences of human breast and colorectal cancers. Science 2006; 314(5797): 268-74.
129. Concu R, Dea-Ayuela MA, Perez-Montoto LG, et al. 3D entropy and moments prediction of enzyme classes and experimentaltheoretic study of peptide fingerprints in Leishmania parasites. Biochim Biophys Acta 2009; 1794(12): 1784-94.
130. Gonzalez-Diaz H, Prado-Prado FJ, Garcia-Mera X, Alonso N, Abeijon P, Caamano O, Yanez M, Munteanu CR, Pazos Sierra A, Dea-Ayuela MA, Gomez-Munoz MT, Garijo MM, Sansano J, Ubeira FM. MIND-BEST: web server for drugs & target discovery; design, synthesis, and assay of MAO-B inhibitors and theoreticexperimental study of G3PD protein from Trichomona gallineae. J Proteome Res 2010; 10(4): 1698-718.
131. Gonzalez-Diaz H, Prado-Prado F, Sobarzo-Sanchez E, Haddad M, Chevalley SM, Valentin A, Quetin-Leclercq J, Dea-Ayuela MA, Teresa Gomez-Munos M, Munteanu CR, Jose Torres-Labandeira J, Garcia-Mera X, Tapia RA, Ubeira FM. NL MIND-BEST: A web server for ligands and proteins discovery-Theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum. J Theor Biol 2011; 276(1): 229-49.
132. Rodriguez-Soca Y, Munteanu CR, Dorado J, Pazos A, Prado-Prado FJ, Gonzalez-Diaz H. Trypano-PPI: a web server for prediction of unique targets in trypanosome proteome by using electrostatic parameters of protein-protein interactions. J Proteome Res 2010; 9(2): 1182-90.
133. Gonzalez-Diaz H, Muino L, Anadon AM, Romaris F, Prado-Prado FJ, Munteanu CR, Dorado J, Sierra AP, Mezo M, Gonzalez- Warleta M, Garate T, Ubeira FM. MISS-Prot: web server for self/non-self discrimination of protein residue networks in parasites; theory and experiments in Fasciola peptides and Anisakis allergens. Mol BioSyst 2011; 7(6): 1938-55.