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dc.contributor.authorMazzatorta, P.*
dc.contributor.authorBenfenati, E.*
dc.contributor.authorNeagu, Daniel*
dc.contributor.authorGini, G.*
dc.date.accessioned2009-10-05T08:19:37Z
dc.date.available2009-10-05T08:19:37Z
dc.date.issued2002
dc.identifier.citationMazzatorta, P. Benfenati, E., Neagu, D.C. and Gini, G. (2002). The importance of scaling in data mining for toxicity prediction. Journal of Chemical Information and Computer Sciences. Vol. 4, No. 5, pp. 1250-1255.en
dc.identifier.urihttp://hdl.handle.net/10454/3594
dc.descriptionNoen
dc.description.abstractWhile 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.en
dc.language.isoenen
dc.subjectData miningen
dc.subjectScalingen
dc.subjectQSARen
dc.subjectQualitative Structure-Activity Relationshipen
dc.subjectChemicals toxicityen
dc.titleThe importance of scaling in data mining for toxicity prediction.en
dc.status.refereedYesen
dc.typeArticleen
dc.type.versionNo full-text available in the repositoryen
dc.identifier.doihttps://doi.org/10.1021/ci025520n


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