Predictive toxicology is concerned with the task of building models capable of determining, with a certain degree of accuracy, the toxicity of chemical compounds. We discuss several machine learning methods that have been applied to build predictive toxicology models. In particular, we present two lazy learning lazy learning techniques applied to the task of predictive toxicology. While most ML techniques use structure relationship models to represent chemical compounds, we introduce a new approach based on the chemical nomenclature to represent chemical compounds. In our experiments we show that both models, SAR and ontology-based, have comparable results for the predictive toxicology task.
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© 2004 Springer
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Armengol, E., Plaza, E. (2004). Lazy Learning for Predictive Toxicology based on a Chemical Ontology. In: Dubitzky, W., Azuaje, F. (eds) Artificial Intelligence Methods And Tools For Systems Biology. Computational Biology, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-5811-0_1
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DOI: https://doi.org/10.1007/978-1-4020-5811-0_1
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-2859-5
Online ISBN: 978-1-4020-2865-6
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