The importance of scaling in data mining for toxicity prediction

Paolo Mazzatorta, Emilio Benfenati, Daniel Neagu, Giuseppina Gini

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)1250-1255
Number of pages6
JournalJournal of Chemical Information and Computer Sciences
Volume42
Issue number5
DOIs
Publication statusPublished - Sep 2002

ASJC Scopus subject areas

  • Chemistry(all)
  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Fingerprint Dive into the research topics of 'The importance of scaling in data mining for toxicity prediction'. Together they form a unique fingerprint.

Cite this