TitleDiscovering plausible explanations of carcinogenenecity in chemical compounds
Publication TypeConference Paper
Year of Publication2007
AuthorsArmengol E
EditorPerner P.
Conference NameLecture Notes in Artificial Intelligence
Volume4571
Publisherspringer
Pagination756-769
Abstract

The goal of predictive toxicology is the automatic construction of carcinogenecity models. Most common artificial intelligence techniques used to construct these models are inductive learning methods. In a previous work we presented an approach that uses lazy learning methods for solving the problem of predicting carcinogenecity. Lazy learning methods solve new problems based on their similarity to already solved problems. Nevertheless, a weakness of these kind of methods is that sometimes the result is not completely understandable by the user. In this paper we propose an explanation scheme for a concrete lazy learning method. This scheme is particularly interesting to justify the predictions about the carcinogenesis of chemical compounds.