Knowledge-Based Learning in Exploratory Science: Learning Rules to Predict Rodent Carcinogenicity

Abstract

In this paper, we report on a multi-year collaboration among computer scientists, toxicologists, chemists, and a statistician, in which the RL induction program was used to assist toxicologists in analyzing relationships among various features of chemical compounds and their carcinogenicity in rodents. Our investigation demonstrated the utility of knowledge-based rule induction in the problem of predicting rodent carcinogenicity and the place of rule induction in the overall process of discovery. Flexibility of the program in accepting different definitions of background knowledge and preferences was considered essential in this exploratory effort. This investigation has made significant contributions not only to predicting carcinogenicity and non-carcinogenicity in rodents, but to understanding how to extend a rule induction program into an exploratory data analysis tool.