title: Knowledge discovery and data mining in toxicology.
abstract: Knowledge discovery and data mining tools are gaining increasing importance for the analysis of toxicological databases. This paper gives a survey of algorithms, capable to derive interpretable models from toxicological data, and presents the most important application areas. The majority of techniques in this area were derived from symbolic machine learning, one commercial product was developed especially for toxicological applications. The main application area is presently the detection of structure-activity relationships, very few authors have used these techniques to solve problems in epidemiological and clinical toxicology. Although the discussed algorithms are very flexible and powerful, further research is required to adopt the algorithms to the specific learning problems in this area, to develop improved representations of chemical and biological data and to enhance the interpretability of the derived models for toxicological experts.
authors.affiliation: Institute for Computer Science, University of Freiburg, Germany. helma@informatik.uni-freiburg.de
authors.forename: C
authors.initials: C
authors.lastname: Helma
authors.forename: E
authors.initials: E
authors.lastname: Gottmann
authors.forename: S
authors.initials: S
authors.lastname: Kramer
mesh_terms: D000465:Algorithms; D000818:Animals; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D005103:Expert Systems; D006801:Humans; D016247:Information Storage and Retrieval; D019359:Knowledge; D012984:Software; D014116:Toxicology