title: Data mining and knowledge discovery in predictive toxicology.
abstract: This article describes the knowledge discovery process in predictive toxicology. This process consists of five major steps (i) feature calculation, (ii) feature selection, (iii) model induction, (iv) model validation and (v) interpretation of predictions and models. Data mining is a part of the knowledge discovery process and consists of the application of data analysis and discovery algorithms, which can be useful in all of the above steps. A brief review of suitable algorithms and their advantages and disadvantages is given for each knowledge discovery step, followed by a more detailed description of a problem-specific implementation of the lazar prediction system.
authors.affiliation: Institute of Computer Science, Georges Kohler Allee 79, D-79110 Freiburg, Germany. helma@informatik.uni-freiburg.de
authors.forename: C
authors.initials: C
authors.lastname: Helma
mesh_terms: D000465:Algorithms; D000818:Animals; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D005544:Forecasting; D006801:Humans; D016247:Information Storage and Retrieval; D019359:Knowledge; D012955:Societies, Medical; D014116:Toxicology