Genetic programming for the induction of decision trees to model ecotoxicity data

Journal of Chemical Information and Modeling

Volumn 45, Issue 4, 2005, Pages 904-912

  Buontempo, Frances V ^a   Wang, Xue Zhong ^a   Mwense, Mulaisho ^a   Horan, Nigel ^a   Young, Anita ^b   Osborn, Daniel ^c  

^a UNIVERSITY OF LEEDS   (United Kingdom)

^b ASTRAZENECA   (United Kingdom)

^c CENTRE FOR ECOLOGY AND HYDROLOGY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGAE; CHEMICAL OXYGEN DEMAND; COMPUTER PROGRAMMING; DECISION THEORY; ECOSYSTEMS; GENETIC ALGORITHMS; GREEN'S FUNCTION; PROBLEM SOLVING; STATISTICAL METHODS; TOXICITY;

AUTOMATIC INDUCTION; DECISION TREES; ECOTOXICITY DATA; GENETIC PROGRAMMING;

ENVIRONMENTAL TESTING;

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; DECISION TREE; POLLUTANT; STRUCTURE ACTIVITY RELATION; THEORETICAL MODEL; TIME;

ALGORITHMS; DECISION TREES; ENVIRONMENTAL POLLUTANTS; MODELS, GENETIC; MODELS, THEORETICAL; STRUCTURE-ACTIVITY RELATIONSHIP; TIME FACTORS;

EID: 23844450432     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci049652n     Document Type: Article

References (60)

1. SOCSA Specialised Organic Chemicals Sector Association, http://www.socsa.org.uk/1993.
2. DEFRA; Environmental Protection Expenditure by Industry: SIC 24: Chemicals and Chemical Products Sector (The Press Release, No. 328/04) ed.; DEFRA: 2002.
3. SEPA Policy Number 37: Aquatic Discharge Compliance Assessment Directive, http://www.sepa.org.uk/pdf/policies/37.pdf 1999.
4. LHASA DEREK software, http://www.chem.leeds.ac.uk/luk/.
5. LogiChem Oncologic software, http://www.logichem.com/.
6. CompuDrug HazardExpert software, http://www.compudrug.com/.
7. Accelrys see http://www.accelrys.com/.
8. Bahler, D.; Stone, B.; Wellington, C.; Bristol, D. Symbolic, neural, and Bayesian machine learning models for predicting carcinogenicity of chemical compounds. J. Chem. Inf. Comput. Sci. 2000, 40, 906-914.
9. Basak, S. C.; Grunwald, G. D.; Gute, B. D.; Balasubramanian, K.; Opitz, D. Use of statistical and neural net approaches in predicting toxicity of chemicals. J. Chem. Inf. Comput. Sci. 2000, 40, 885-890.
10. Burden, F. R.; Winkler, D. A. Robust QSAR Models Using Bayesian Regularised Artificial Neural Networks. J. Med. Chem. 1999, 42, 3183-3187.
11. Gini, G.; Lorenzini, M.; Benfenati, E.; Grasso, P.; Bruschi, M. Predictive carcinogenicity: A model for aromatic compounds, with nitrogen-containing substituents, based on molecular descriptors using an artificial neural network. J. Chem. Inf. Comput. Sci. 1999, 39, 1076-1080.
12. Kaiser, K. L. E.; Niculescu, S. P. Using probabilistic neural networks to model the toxicity of chemicals to the fathead minnow (Pimephales promelas): A study based on 865 compounds. Chemosphere 1999, 38, 3237-3245.
13. Kovesdi, I.; Dominguez-Rodriguez, M. F.; Orfi, L.; Naray-Szabo, G.; Varro, A.; Papp, J. G.; Matyus, P. Application of neural networks in structure - activity relationships. Med. Res. Rev. 1999, 19, 249-269.
14. Niculescu, S. P.; Kaiser, K. L. E.; Schultz, T. W. Modeling the toxicity of chemicals to Tetrahymena pyriformis using molecular fragment descriptors and probabilistic neural networks. Archives Environ. Contam. Toxicol. 2000, 39, 289-298.
15. Vracko, M. A study of structure carcinogenic potency relationship with artificial neural networks. The using of descriptors related to geometrical and electronic structures. J. Chem. Inf. Comput. Sci. 1997, 37, 1037-1043.
16. ECETOC. QSARs in the assessment of the environmental fate and effects of chemicals, European Centre for Ecotoxicology and Toxicology of Chemicals, 1998.
17. Benfenati, E.; Gini, G. Computational predictive programs (expert systems) in toxicology. Toxicology 1997, 119, 213-225.
18. Benigni, R. The first US National Toxicology Program exercise on the prediction of rodent carcinogenicity: Definitive results. Mutat. Res.-Rev. Mutat. Res. 1997, 387, 35-45.
19. Cariello, N. F.; Wilson, J. D.; Britt, B. H.; Wedd, D. J.; Burlinson, B.; Gombar, V. Comparison of the computer programs DEREK and TOPKAT to predict bacterial mutagenicity. Mutagenesis 2002, 17, 321-329.
20. Murthy, S. K. Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey. Data Min. Knowledge Discovery 1998, 2, 345-389.
21. Worth, A. P.; Cronin, M. T. D. The use of discriminant analysis, logistic regression and classification tree analysis in the development of classification models for human health effects. J. Mol. Struct.: THEOCHEM 2003, 622, 97-111.
22. Michalski, R. S. Selection of most representative training examples and incremental generation of VLI hypotheses: the underlying method and description of programs ESEL and AQ11, Computer Science Department, University of Illinois, 1978.
23. Michalski, R. S.; Chilausky, R. L. Knowledge acquisition by encoding expert rules versus computer induction from examples: a case study involving soybean pathology. Int. J. Human-Comput. Stud. 1999, 51, 239-263.
24. Mitchell, T. M. In Proceedings of IJCAI-87; Cambridge, Mass, 1977.
25. Mitchell, T. M. Machine Learning; International Editions; McGraw-Hill: New York, London, 1997.
26. Breiman, L.; Freidman, J. H.; Olshen, R. A.; Stone, C. J. Classification and regression trees; Chapman & Hall/CRC: Boca Raton, London, 1993.
27. Tong, W.; Hong, H.; Fang, H.; Xie, Q.; Perkins, R. Decision forest: combining the predictions of multiple independent decision tree models. J. Chem. Inf. Comput. Sci. 2003, 43, 525-531.
28. Rusinko, A.; Farmen, M. W.; Lambert, C. G.; Brown, P. L.; Young, S. S. Analysis of a large structure/biological activity data set using recursive partitioning. J. Chem. Inf. Comput. Sci. 1999, 39, 1017-1026.
29. Quinlan, J. R. C4.5: programs for machine learning; Morgan Kaufmann: San Mateo, CA, 1993.
30. Yuan, B. Ph.D., Leeds University, 2002.
31. Liu, H.; Hussain, F.; Tan, C. L.; Dash, M. Discretization: An enabling technique. Data Min. Knowledge Discovery 2002, 6, 393-423.
32. Klopman, G. Artificial-Intelligence Approach to Structure Activity Studies - Computer Automated Structure Evaluation of Biological-Activity of Organic-Molecules. J. Am. Chem. Soc. 1984, 106, 7315-7321.
33. Bahler, D.; Bristol, D. In Intelligent Systems for Molecular Biology; Hunter, L., Shavlik, J., Searls, D., Eds.; AAAI/MIT Press: Menlo Park, CA, 1993; pp 29-37.
34. Glymour, C.; Madigan, D.; Pregibon, D.; Smyth, P. Statistical themes and lessons for data mining. Data Min. Knowledge Discovery 1997, 1, 11-28.
35. Lee, Y.; Buchanan, B. G.; Aronis, J. M. Knowledge-based learning in exploratory science: Learning rules to predict rodent carcinogenicity. Machine Learning 1998, 30, 217-240.
36. Lee, Y.; Buchanan, B. G.; Mattison, D. M.; Klopman, G.; Rosenkranz, H. S. Learning rules to predict rodent carcinogenicity of nongenotoxic chemicals. Mutat. Res./Fundam. Mol. Mech. Mutagen. 1995, 328, 127-149.
37. Klopman, G.; Saiakhov, R.; Rosenkranz, H. S. Multiple computer-automated structure evaluation study of aquatic toxicity II. Fathead minnow. Environ. Toxicol. Chem. 2000, 19, 441-447.
38. MultiCASE available at http://www.multicase.com/.
39. Dougherty, J.; Kohavi, R.; Sahami, M. In Twelfth International Conference on Machine Learning; Russell, A. P. a. S., Ed.; Morgan Kaufmann: 1995.
40. Quinlan, J. R. Simplifying decision trees. Int. J. Human-Comput. Stud. 1999, 51, 497-510.
41. DeLisle, R. K.; Dixon, S. L. Induction of Decision Trees via Evolutionary Programming. J. Chem. Inf. Comput. Sci. 2004, 44, 862-870.
42. Bala, J.; Huang, J.; Vafaie, H.; DeJong, K.; Wechsler, H. In IJCAI; Montreal, 1995.
43. Dutton, D. M.; Conroy, G. V. A review of machine learning. Knowledge Eng. Rev. 1997, 12, 341-367.
44. Takagi, H. In Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks and Genetic Algorithms; Ruan, D., Ed.; Kluwer Academic Press: Massachusetts, 1997; pp 1-33.
45. Chiu, T.-L.; So, S.-S. Genetic Neural Networks for Functional Approximation. Quant. Struct - Act. Relat. Comb. Sci. 2003, 22, 519-526.
46. Bot, M. C. J. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000); Morgan Kaufmann: 2000; pp 403-410.
47. Tropsha, A.; Gramatica, P.; Gombar, V. K. The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models. QSAR Comb. Sci. 2003, 22, 69-77.
48. Wold, S.; Eriksson, L. In Chemometrics Methods in Molecular Design; Waterbeemd, H. v. d., Ed.; VCH: Weinheim, 1995; pp 309-318.
49. Hoai, N. X.; McKay, R. I. B.; Essam, D.; Abbass, H. A. In Genetic Programming: 7th European Conference, EuroGP 2004, Coimbra, Portugal, April 5-7, 2004. Proceedings; Keijzer, M., O'Reilly, U.-M., Lucas, S. M., et al., Eds.; Springer-Verlag: Heidelberg, 2004; Vol. 3003, pp 67-77.
50. Koza, J. R. Genetic Programming: On the Programming of Computers by Means of Natural Selection; The MIT Press: 1992.
51. GP Genetic Programming organisation, http://www.genetic-programming.org/ 2005.
52. Pujol, J. C. F.; Poli, R. Evolution of the Topology and the Weights of Neural Networks using Genetic Programming with a Dual Representation, University of Birmingham, School of Computer Science, 1997.
53. Nikolaev, N.; Slavov, V. In First European Workshop on Genetic Programming; Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T., Eds.; Springer: Europe, 1998; Vol. 1391, pp 49-59.
54. Loveard, T.; Ciesielski, V. Representing Classification Problems in Genetic Programming. Proc. Congress Evol. Comput. 2001, 2, 1070-1077.
55. Marmelstein, R. E.; Lamont, G. B. In Proceedings of the Third Annual Conference on Genetic Programming; Morgan Kaufmann: 1998; pp 223-231.
56. Zhao, Y. H.; Cronin, M. T. D.; Dearden, J. C. Quantitative structure -activity relationships of chemicals acting by nonpolar narcosis -Theoretical considerations. Quant. Struct.-Act. Relat. 1998, 17, 131-138.
57. Dragon http://www.disat.unimib.it/chm/Dragon.htm, 2004.
58. Cronin, M. T. D.; Netzeva, T. I.; Dearden, J. C.; Edwards, R.; Worgan, D. P. Assessment and Modeling of the Toxicity of Organic Chemicals to Chlorella vulgaris: Development of a Novel Database. Chem. Res. Toxicol. 2004, 17, 545-554.
59. Cronin, M. T. D.; Schultz, T. W. Pitfalls in QSAR. J. Mol. Struct.-THEOCHEM 2003, 622, 39-51.
60. Konemann, H. Quantitative Structure - Activity-Relationships in Fish Toxicity Studies. 1. Relationship for 50 Industrial Pollutants. Toxicol. 1981, 19, 209-221.