E-modelling: Foundations and cases for applying AI to life sciences

International Journal on Artificial Intelligence Tools

Volumn 16, Issue 2, 2007, Pages 243-268

  Gini, Giuseppina ^a   Benfenati, Emilio ^b  

^a POLITECNICO DI MILANO   (Italy)

^b MARIO NEGRI INSTITUTE FOR PHARMACOLOGICAL RESEARCH   (Italy)

Author keywords

Biological data; Modeling; Prediction

Indexed keywords

BIOLOGY; COMPUTATIONAL COMPLEXITY; DATA ACQUISITION; DATA REDUCTION; PROBLEM SOLVING;

BIOLOGICAL DATA; COMPUTATIONAL TOXICITY (CT); PREDICTION; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS (QSAR);

INTERNET;

EID: 34247355586     PISSN: 02182130     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218213007003308     Document Type: Article

References (27)

1. L. Hunter (ed.), Artificial Intelligence and MolecularBiology (Cambridge, MA: MIT Press, 1993).
2. G. C. Gini and A. R. Katritzky (eds.), Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools (AAAI Press, Menlo Park, CA, USA, 1999).
3. B. G: Buchanan, E. A. Feigenbaum, DENDRAL and Meta-DENDRAL: Their Applications Dimension, Artificial Intelligence 11, (1978) pp.5-24.
4. R. K. Lindsay, B. G. Buchanan, E. A. Feigenbaum, J. Lederberg, J. DENDRAL: A Case Study of the First Expert System for Scientific Hypothesis Formation, Artificial Intelligence 61 (1993) pp. 209-261.
5. E. Benfenati, N. Piclin, A. Roncaglioni'M. R. Varl. Factors Influencing Predictive Models For Toxicology, SAR and QSAR in environmental research 12 (2001), pp. 593-603
6. C. Hansch, D. Hoekman, A. Leo, L. Zhang, P. Li, The expanding role of quantitative structure-activity relationships (QSAR) in toxicology, Toxicology Letters 79 (1995), pp. 45-53.
7. Chemical Hazard Data Availability Study from EPA (Environmental Protection Agency), http://www.epa.gov/opptintr/chemtest/hazchem.htm.
8. Press release, http://europa.eu.int/comm/environment/chemicals/ whitepaper.htm
9. P. Langley, H. A. Simon, G. L. Bradshaw, J. M. Zytkow, Scientific Discovery: Computational Explorations of the Creative Process (Cambridge, Massachusetts: MIT Press, 1987).
10. 2! Journal of Molecular Graphics and Modelling 20 (2002) pp. 269-276.
11. D. M. Hawkins, The problem of overfitting. J of Chemical Information and Computer Sciences 44 (2004), pp. 1-12.
12. G. M. Rand, Fundamentals of aquatic Toxicology; effects, environmental fate and risk assessment, (CRC Press, 1995).
13. P. Suppes, Models of data, in: Logic, Methodology and the Philosophy of Science: Proceedings of the 1960 International Congress, E. Nagel, P. Suppes and A. Tarski (eds) (Stanford University Press, Stanford, CA, 1962) pp. 252-261.
14. G. C. Gini, E. Benfenati, M. Lorenzini, M. Bruschi, P. Grasso, 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 Sciences 39 (1999) pp. 1076-1080.
15. C. L. Russom, S. P. Bradbury, S. J. Broderius, D. E. Hammermeister, R. A. Drummond, Predicting modes of action from chemical structure: Acute toxicity in the Fathead Minnow (Pimephales Promelas), Environmental Toxicology and Chemistry 16 (1997) pp. 948-957
16. A. R. Katritzky, V. S. Lobanov, M. Karelson, CODESSA: Comprehensive Descriptors for Structural and Statistical Analysis, version 2.2.1. Reference Manual. University of Florida (Gainesville, Florida, U.S.A, 1994).
17. R. D. Cramer, D.E. Patterson, J.D. Bunce, Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 110, 18 (1988) pp. 5959-67.
18. R. Singh, Reasoning About Molecular Similarity and Properties, Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference (CSB 2004)
19. M. Berthold, D. J. Hand, Intelligent Data Analysis - An introduction (Springer, Berlin, 1999).
20. C. König, G. C. Gini, M. Craciun, E. Benfenati, Multi-class classifier from a combination experts: towards distributed computation real-problem classifiers. International Journal of Pattern Recognition and Artificial Intelligence 18, 5 .(2004) pp. 801-817.
21. R. Kohavi and G. H. John, Wrappers for Feature Subset Selection, Artificial Intelligence, 97 (1-2) (1996) pp. 273-324.
22. D. H. Wolpert, Stacked Generalization, Neural Networks, 5,. (1992) pp. 241-259.
23. A. J. Sharkey, Combining Artificial Neural Nets - Ensemble and Modular Multi-Net Systems Springer, London, 1999.
24. R. Quinlan, C4.5: Programs for Machine Learning (Morgan Kaufmann Publishers, San Mateo, CA, 1993)
25. C-D. Neagu and G. C. Gini, Neuro-Fuzzy knowledge integration applied to toxicity prediction, in Innovations in Knowledge Engineering, R. Jain, A. Abraham, C. Faucher, B. Jan van der Zwaag (eds), Advanced Knowledge International Pty Ltd, Australia, 2003.
26. L. Breiman et al., "Classification and Regression Trees (CART)", Wadsworth & Brooks, 1984.
27. J. Mingers, An Empirical Comparison of Pruning Methods for Decision Tree Induction, Machine Learning, 4, (1989) pp. 227-243.