Predicting toxicity against the fathead minnow by Adaptive Fuzzy Partition

QSAR and Combinatorial Science

Volumn 22, Issue 2, 2003, Pages 210-219

  Pintore, Marco ^a   Piclin, Nadège ^b,c   Benfenati, Emilio ^c   Gini, Giuseppina ^d   Chrétien, Jacques R ^a,b  

^a BioChemics Consulting   (France)

^b UNIVERSITÉ D'ORLÉANS   (France)

^c MARIO NEGRI INSTITUTE FOR PHARMACOLOGICAL RESEARCH   (Italy)

^d POLITECNICO DI MILANO   (Italy)

Author keywords

Ecotoxicity; Fathead minnow; Fuzzy logic; SAR models

Indexed keywords

COMPUTER CIRCUITS; CONFORMAL MAPPING; FORECASTING; GENETIC ALGORITHMS; SELF ORGANIZING MAPS; TOXICITY;

2D STRUCTURES; 3D STRUCTURE; ADAPTIVE-FUZZY; ECOTOXICITY; FATHEAD MINNOW; FUZZY PARTITION; FUZZY-LOGIC; MOLECULAR DESCRIPTORS; RECENT PROGRESS; SAR MODEL;

FUZZY LOGIC;

ALCOHOL DERIVATIVE; ALDEHYDE DERIVATIVE; ALKANE DERIVATIVE; AMINE; AROMATIC COMPOUND; ESTER DERIVATIVE; KETONE DERIVATIVE; NITROGEN DERIVATIVE; ORGANOPHOSPHORUS COMPOUND; SULFUR DERIVATIVE; TOXIC SUBSTANCE;

ADAPTIVE FUZZY PARTITION; ALGORITHM; CHEMICAL ANALYSIS; CHEMICAL STRUCTURE; CONFERENCE PAPER; CONTROLLED STUDY; ECOTOXICITY; FISH; METHODOLOGY; NONHUMAN; PREDICTION; PRIORITY JOURNAL; STRUCTURE ACTIVITY RELATION; STRUCTURE ANALYSIS; TOXICITY; TOXICOLOGICAL PARAMETERS; VALIDATION PROCESS;

ANIMALIA; PHOXINUS PHOXINUS; PIMEPHALES PROMELAS;

EID: 0038745832     PISSN: 1611020X     EISSN: None     Source Type: Journal    
DOI: 10.1002/qsar.200390014     Document Type: Conference Paper

References (49)

1. Jamet, P. (Ed.), European Commission. Directorate-Generale XII. COST 66. Fate of pesticides in the soil and the environment, ECSP-EEC-EAEC, Brussels 1994.
2. Karcher, W., and Devillers, J. (Eds.), Practical Applications of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology, Kluwer Academic Publishers, Dordrecht (The Netherlands) 1990.
3. Dearden, J. C., Barratt, M. D., Benigni, R., Bristol, D. W., Combes, R. D., Cronin, M. T. D., Judson, P. N., Payne, M. P., Richard, A. M., Tichy, M., Worth, A. P., and Yourick, J. J., The Development and Validation of Expert Systems for Predicting Toxicity, ATLA 25, 223-252 (1997).
4. Gini, G., and Katrizky A., Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools, AAAI Press, Menlo Park (USA) 1999.
5. Todeschini, R., and Consonni, V., Handbook of Molecular Descriptors, Wiley-VCH, Mannheim (Germany) 2000.
6. Ros, F., Guillaume, S., Rabatel, G., and Sevila, F., Recognition of overlapping particles in granular product images using statistics and neural networks, Food Control 6, 37-43 (1995).
7. Ventura, S., Silva, M., Pérez-Bendito, D., and Hervás C., Computational Neural Networks in Conjunction with Principal Component Analysis for Resolving Highly Nonlinear Kinetics, J. Chem. Inf. Comp. Sci. 37, 287-291 (1997).
8. Gini, G., Lorenzini, M., Benfenati, E., Grasso, P., and 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 Comp. Sci. 39, 1076-1080 (1999).
9. Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, New York (USA) 1989.
10. Collins, R. J., and Jefferson, D. R., Selection in massively parallel genetic algorithms, in: Belew, R. K., and Booker, L. B. (Eds.), Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, San Mateo (USA) 1991, pp. 244-248.
11. Wold, S., PLS for Multivariate linear Modeling, in: van de Waterbeemd, H. (Ed.), Chemometric Methods in Molecular Design, VCH, Weinheim (Germany) 1995, pp. 195-218.
12. Hecht-Nielsen, R., Theory of the backpropagation neural network, in: Proceedings of the International Joint Conference on Neural Networks, IEEE Press, New York (USA) 1989, pp. 593-605.
13. Rorije, E., and Peijnenburg, W. J. G. M., QSARs for oxidation of phenols in the aqueous environment, suitable for risk assessment, J. Chemom. 10, 79-93 (1996).
14. Verhaar, H. J. M., Urrestarazu Ramos, E., and Hermens, J. L. M., Classifying environmental pollutants. 2: Separation of class 1 (baseline toxicity) and class 2 ('polar narcosis') type compounds based on chemical descriptors, J. Chemom. 10, 149-162 (1996).
15. Devillers, J., A general QSAR model for predicting the acute toxicity of pesticides to Lepomis macrochirus, SAR QSAR Environ. Res. 11, 397-417 (2001).
16. Devillers, J., QSAR modeling of large heterogeneous sets of molecules, SAR QSAR Environ. Res. 12, 515-528 (2001).
17. /365/CE: Commission Decision of 25 July 1995 establishing the ecological criteria for the award of the Community ecolabel to laundry detergents. Official Journal NO. L217, 13/09/ 1995, pp. 0014-0030.
18. Benfenati, E., Piclin, N., Roncaglioni, A., and Varì M. R., Factors influencing predictive models for toxicology, SAR QSAR Environ. Res. 12, 593-603 (2001).
19. Schalkoff, R., Pattern recognition statistical, structural and neural approaches, John Wiley & Sons, New York (USA) 1992.
20. Zadeh, L. A., 1977. Fuzzy sets and their applications to classification and clustering, in: Van Ryzin, J. (Ed.), Classification and Clustering, Academic Press, New York (USA) 1977, pp. 251-299.
21. Pintore, M., Audouze, K., Ros, F., and Chrétien, J. R., Adaptive fuzzy partition in data base mining: application to olfaction, Data Science Journal 1, 99-110 (2002).
22. Pintore, M., Piclin, N., Benfenati, E., Gini, G., and Chrétien, J. R., Database mining with adaptive fuzzy partition (AFP): application to the prediction of pesticide toxicity on rats, Environ. Toxicol. Chem., in press (2002).
23. Brooke, L. T., Call, D. J., Geiger, D. L., and Northcott, C. E. (Eds.), Acute Toxicities of Organic Chemicals to Fathead Minnows (Pimephales promelas), Vol. 1, Center for Lake Superior Environmental Studies, University of Wisconsin, Superior (USA) 1984.
24. Geiger, D. L., Northcott, C. E. Call, D. J., and Brooke, L. T. (Eds.), Acute Toxicities of Organic Chemicals to Fathead Minnows (Pimephales promelas), Vol. 2, Center for Lake Superior Environmental Studies, University of Wisconsin, Superior (USA) 1985.
25. Geiger, D. L., Poirier, S. H., Brooke, L. T., and Call, D. J. (Eds.), Acute Toxicities of Organic Chemicals to Fathead Minnows (Pimephales promelas), Vol. 3, Center for Lake Superior Environmental Studies, University of Wisconsin, Superior (USA) 1986.
26. Geiger, D. L., Call, D. J., and Brooke, L. T. (Eds.), Acute Toxicities of Organic Chemicals to Fathead Minnows (Pimephales promelas), Vol. 4, Center for Lake Superior Environmental Studies, University of Wisconsin, Superior (USA) 1988.
27. Geiger, D. L., Brooke, L. T. and Call, D. J. (Eds.), Acute Toxicities of Organic Chemicals to Fathead Minnows (Pimephales promelas), Vol. 5. Center for Lake Superior Environmental Studies, University of Wisconsin, Superior (USA) 1990.
28. Russom, C. L., Bradbury, S. P., Broderius, S. J., Hammermeister D. E., and Drummond R. A., Predicting modes of action from chemical structure: Acute toxicity in the fathead minnow (Pimephales promelas), Environ. Toxicol. Chem. 16, 948-967 (1997).
29. Directive 92/32/ECC (1992), the seventh amendment to Directive 67/548/ECC, OJL 154 of 5.VI.92, pl.
30. Ros, F., Pintore, M., and Chrétien, J. R., Molecular descriptor selection combining genetic algorithms and fuzzy logic: application to database mining procedures, Chemometr. Intell. Lab. 63, 15-26 (2002).
31. Kohonen, T., Self-Organizing Maps, Springer-Verlag, Berlin (Germany) 2001.
32. Kier, L. B., and Hall, L. H., Molecular connectivity in structure analysis, Wiley, New York (USA) 1986.
33. Dearden, J. C., Physico-chemical descriptors, in: Karcher, W., and Devillers, J. (Eds.), Practical Applications of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology, Kluwer Academic, Dordrecht (The Netherlands) 1990, pp. 25-29.
34. Sabljic, A., Topological indices and environmental chemistry, in: Karcher, W., and Devillers, J. (Eds.), Practical Applications of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology, Kluwer Academic, Dordrecht (The Netherlands) 1990, pp. 61-82.
35. Kier, L. B., and Hall, L. H., Molecular Structure Description -The Electrotopological State, Academic Press, San Diego (USA) 1999.
36. ChemInter© 1.0, ChemInter.
37. SciQSAR 2D®, SciVision, Burlington (USA) 1999.
38. PALLAS© 2.0, CompuDrug Chemistry Ltd., Budapest (Hungary) 1994-1995.
39. Pintore, M., Taboureau, O., Ros, F., and Chrétien J. R., Data Base Mining (DBM) applied to CNS activity, Eur. J. Med. Chem. 36, 349-359 (2001).
40. HyperChem 5.1, Hypercube Inc., Gainesville (USA) 1997.
41. CODESSA 2.20, Semichem Inc., Shawnee Mission (USA) 1994-1996.
42. Kinnear, K. E., Advances in Genetic Programming, MIT Press, Cambridge (USA) 1994.
43. Haupt, R. L., and Haupt S. E., Practical Genetic Algorithms, Wiley, New York (USA) 1999.
44. Leardi, R., and Gonzales A. L., Genetic algorithms applied to feature selection in PLS regression: how and when to use them, Chemometr. Intell. Lab. 41, 195-207 (1998).
45. Lin, Y., and Cunninghan, G. J., Building a Fuzzy System from Input-Output Data, J. Intell. Fuzzy Syst. 2, 243-250 (1994).
46. Sugeno, M., and Yasakawa, T., A fuzzy-logic-based approach to qualitative modeling, IEEE T. Fuzzy Syst. 1, 7-31 (1993).
47. Dubois, D., and Prade, H., An introduction to possibilistic and fuzzy logics, in: Shafer, G., and Pearl, J. (Eds.), Readings in Uncertain Reasoning, Morgan Kaufmann, San Francisco (USA) 1990, pp. 742-761.
48. Gupta, M. M., and Qi, J., Theory of T-norms and fuzzy inference methods, Fuzzy Set Syst. 40, 431-450 (1991).
49. Hermens, J. L. P., Quantitative structure-activity relationships for predicting fish toxicity, in: Karcher, W., and Devillers, J. (Eds.), Practical Applications of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology, Kluwer Academic, Dordrecht (The Netherlands) 1990, pp. 263-280.