Hybrid intelligent systems for predictive toxicology - A distributed approach

Proceedings - 5th International Conference on Intelligent Systems Design and Applications 2005, ISDA '05

Volumn 2005, Issue , 2005, Pages 26-31

  Neagu, Daniel ^a   Craciun, Marian V ^b   Stroia, Silviu A ^b   Bumbaru, Severin ^b  

^a UNIVERSITY OF BRADFORD   (United Kingdom)

^b DUNAREA DE JOS UNIVERSITY OF GALATI   (Romania)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; DATA MINING; MATHEMATICAL MODELS; OPTIMIZATION; PARALLEL PROCESSING SYSTEMS; TOXICITY;

HYBRID INTELLIGENT SYSTEMS; PREDICTIVE DATA MINING; PREDICTIVE TOXICOLOGY;

INTELLIGENT AGENTS;

EID: 33847037509     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISDA.2005.52     Document Type: Conference Paper

References (39)

1. G. Piatetsky-Shapiro and W. J. Frawley, Eds., Knowledge Discovery in Databases, AAAI/ MIT Press, 1991.
2. U. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy, Eds., Advances in knowledge discovery and data mining, Menlo Park, CA: AAAI/ MIT Press, 1996.
3. S. M. Weiss and N., Indurkhya, Predictive Data Mining - a practical guide, Morgan Kaufmann Publishers, 1998.
4. Y.-t., Woo, "A Toxicologist's View and Evaluation", ECML/PKDD-01 Workshop, The Predictive Toxicology Challenge (PTC) 2000-2001
5. nd ed., G. M. Rand, Ed., Taylor and Francis, Washington, DC, 1995, pp. 609-655.
6. D. Bahler and D. W. Bristol, "Prediction of Chemical Carcinogenicity in Rodents By Machine Learning of Decision Trees and Rule Sets", Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools, 1999 AAAI Spring Symposium, Tech. Rept. SS-99-01, Menlo Park CA: AAAI Press, pp. 74-77.
7. D. Bahler, B. Stone, C. Wellington and D. W. Bristol, "Symbolic, Neural and Bayesian Machine Learning Models for Predicting Carcinogenicity of Chemical Compounds", Journal of Chemical Information and Computer Sciences 8, 2000, pp. 906-914.
8. E. Benfenati and G. Gini, "Computational predictive programs (expert systems) in toxicology", Toxicology 119, 1997, 213-225.
9. G. Gini, "Predictive Toxicology of Chemicals: Experience and Impact of AI tools", AI Magazine 21/3, AAI Press, 2000, pp. 81-84.
10. R. Adamczak and W. Duch, "Neural networks for structureactivity relationship problems", in Procs 5th Conference on Neural Networks and Soft Computing, Zakopane, 2000, pp. 669-674.
11. K. L. E. Kaiser and S. P. Niculescu, "Using probabilistic neural networks to model the toxicity of chemicals to the fathead minnow (Pimephales Promelas): A study based on 865 compounds", Chemosphere 38,1999, pp. 3237-3245.
12. K. L. E. Kaiser and S. P. Niculescu, "Modeling the acute toxicity of chemicals to Daphnia magna: a probabilistic neural network approach", Environ. Toxicol. Chem. 20, 2001, pp.402-431.
13. K. L. E. Kaiser, S. P. Niculescu and T. W. Schultz, "Modeling the toxicity of chemicals to Tetrahymena pyriformis using molecular fragment descriptors and probabilistic neural networks", Archiv. Environ. Toxicol. Chem.39,2000, pp. 289-298.
14. G. Gini and M. Lorenzini, "Predictive Carcinogenicity: A Model for Aromatic Compounds, with Nitrogen-Containing Substituents, Based on Molecular Descriptors Using an Artificial Neural Network", J. of Chemical Information and Computer Sc. 39,1999, pp. 1076-1080.
15. M. Pintore, N. Piclin, E. Benfenati, G. Gini, and J. R. Chretien, "Data Mining with Adaptive Fuzzy Partition: Application to the Prediction of Pesticide Toxicity on Rats", Environmental Toxicology and Chemistry, 22/5, 2002, pp. 983-991.
16. C.D. Neagu, A.O. Aptula and G. Gini, "Neural and neurofuzzy Models of Toxic Action of Phenols", in Procs IEEE International Symposium Intelligent Systems' Methodology, Models, Applications in Emerging Technologies, 2002, Varna, Bulgaria.
17. E. Benfenati, P. Mazzatorta, C.D. Neagu and G. Gini, "Combining Classifiers of pesticides toxicity through a neurofuzzy approach", 3rd International Workshop on Multiple Classifier Systems, 2002, Caligliary, Italy.
18. C. König, G. Gini, M. Craciun and E. Benfenati, "Multi-class classifier from a combination of local experts: toward distributed computation for real-problem classifiers", International Journal on Pattern Recognition and Artificial Intelligence, 18/5, 2004, pp. 801-817.
19. L. Breiman, "Bagging predictors", Machine Learning, vol. 24(2), 1996, pp. 123-140.
20. Y. Freund, "Boosting a weak learning algorithm by majority", Information and Computation, 121(2), 1995, pp. 256-285.
21. R. A. Jacob, M. I. Jordan, S. J. Nowlan and G. E. Hinton, "Adaptive Mixtures of Local Experts", Neural Computation, 3, 1991, pp.79-87
22. D. H. Wolpert, "Stacked Generalization", Neural Networks, 5,1992, pp. 241-259.
23. L. Breiman, "Stacked generalization", Machine Learning, 24(1), 1996, pp. 49-64.
24. R. D. Small, H. A. Edelstein, Scalable Data Mining, White Paper, Two Crows Company, 1997
25. A. A. Freitas, "A Survey of Parallel Data Mining", in Proceedings of the 2nd International Conference on the Practical Applications of Knowledge Discovery and Data Mining, 1998, pp. 287-300.
26. D. Taniar, J. W. Rahayu, "Parallel Data Mining", H. A. Abbas, R. A. Sarker, C. S. Newton, Eds., Data Mining a Heuristic Approach, Idea Group Publishing, 2002.
27. Chem eStandards: http://www.cidx.org/ChemeStandards
28. Chemical Markup Language: http://xml-cml.org
29. ToxML: http://www.leadscope.com/news/.
30. URML: http://home.comcast.net/~stabet/urml.html.
31. KQML: http://www.cs.umbc.edu/kqml/.
32. DAML: http://www.daml.org.
33. CBML: http://www.cs.tcd.ie/research_groups/mlg/CBML/
34. PMML: http://www.dmg.org/index.html.
35. S. Tabet P. Bhogaraju and D. Ash, "Using XML as a Language Interface for AI Applications", in Procs. International Conference PRICAI2000, LNCS 2112, Springer, pp. 103-110.
36. D. Neagu, M. Craciun and Q. Chaudhry, "Knowledge Specification for Versatile Hybrid Intelligent Systems", in A. Lotfi Ed., Procs. of the 5th International Conference on Recent Advances in Soft Computing, Nottingham, UK, Dec. 2004, pp. 219-224.
37. D. Neagu and V. Palade, "Modular neuro-fuzzy networks used in explicit and implicit knowledge integration, in Procs 15th International Conference Florida AI Soc. - FLAIRS2002, pp 277-281.
38. D. Neagu and G. Gini, "Neuro-Fuzzy Knowledge Integration applied in Toxicity Prediction, in Innovations in Knowledge Engineering, Advanced Knowledge International, 2003, pp. 311-342.
39. rd European Conference on Intelligent Systems and Technologies, July 2004, Iasi, Romania, pp. 61-69