title: The importance of scaling in data mining for toxicity prediction.
abstract: While mining a data set of 554 chemicals in order to extract information on their toxicity value, we faced the problem of scaling all the data. There are numerous different approaches to this procedure, and in most cases the choice greatly influences the results. The aim of this paper is 2-fold. First, we propose a universal scaling procedure for acute toxicity in fish according to the Directive 92/32/EEC. Second, we look at how expert preprocessing of the data effects the performance of qualitative structure-activity relationship (QSAR) approach to toxicity prediction.
authors.affiliation: Instituto di Ricerche Farmacologiche Mario Negri Milano, Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Mazzatorta
authors.forename: Emilio
authors.initials: E
authors.lastname: Benfenati
authors.forename: Daniel
authors.initials: D
authors.lastname: Neagu
authors.forename: Giuseppina
authors.initials: G
authors.lastname: Gini
mesh_terms: D000818:Animals; D003198:Computer Simulation; D003530:Cyprinidae; D004353:Drug Evaluation, Preclinical / Q000706:statistics & numerical data; D064420:Drug-Related Side Effects and Adverse Reactions; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship
chemical_list: D004364:Pharmaceutical Preparations