title: Learning rules to predict rodent carcinogenicity of non-genotoxic chemicals.
abstract: The results of short-term assays (induction of chromosomal aberrations and sister-chromatid exchanges, oncogenic transformations and cellular toxicity) together with MTD (maximum tolerated dose) values and physical chemical properties of non-genotoxic (i.e. Salmonella non-mutagens) carcinogens and non-carcinogens were submitted to RL, an inductive learning program. RL was able to learn rules that correctly predicted between 70 and 80% of non-genotoxic chemicals. This is a marked improvement over current predictions using only the results of short-term assays and exceeds the predictions of human experts that used the whole spectrum of acute and subchronic toxicity results as well as human knowledge and intuition.
authors.affiliation: Department of Computer Science, University of Pittsburgh, PA 15260, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Lee
authors.forename: B G
authors.initials: BG
authors.lastname: Buchanan
authors.forename: D M
authors.initials: DM
authors.lastname: Mattison
authors.forename: G
authors.initials: G
authors.lastname: Klopman
authors.forename: H S
authors.initials: HS
authors.lastname: Rosenkranz
mesh_terms: D000818:Animals; D015197:Carcinogenicity Tests; D002273:Carcinogens / Q000633:toxicity*; D002869:Chromosome Aberrations; D016208:Databases, Factual; D005103:Expert Systems; D009153:Mutagens; D011237:Predictive Value of Tests; D012377:Rodentia; D012680:Sensitivity and Specificity; D012854:Sister Chromatid Exchange
chemical_list: D002273:Carcinogens; D009153:Mutagens