TítolLazy Learning for Predictive Toxicology based on a Chemical Ontology
Publication TypeBook Chapter
Year of Publication2005
AuthorsArmengol E, Plaza E
EditorDubitzky W, Azuaje F
Book TitleArtificial Intelligence Methods and Tools for Systems Biology
Paginació1 - 18
EditorSpringer
Resum

Predictive toxicology Predictive toxicology is 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.