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dc.contributor.authorHelma, Christoph
dc.contributor.authorKramer, Stefan
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorGottmann, Eva
dc.coverage.spatialUNITED STATESen_NZ
dc.date.accessioned2008-11-27T20:02:13Z
dc.date.available2008-11-27T20:02:13Z
dc.date.issued2000
dc.identifier.citationHelma, C., Kramer, S., Pfahringer, B., Gottmann, E. (2000). Data Quality in Predictive Toxicology: Identification of Chemical Structures and Calculation of Chemical Descriptors. Environmental Health Perspectives, 18(11), 1029-1033.en_US
dc.identifier.urihttps://hdl.handle.net/10289/1483
dc.description.abstractEvery technique for toxicity prediction and for the detection of structure–activity relationships relies on the accurate estimation and representation of chemical and toxicologic properties. In this paper we discuss the potential sources of errors associated with the identification of compounds, the representation of their structures, and the calculation of chemical descriptors. It is based on a case study where machine learning techniques were applied to data from noncongeneric compounds and a complex toxicologic end point (carcinogenicity). We propose methods applicable to the routine quality control of large chemical datasets, but our main intention is to raise awareness about this topic and to open a discussion about quality assurance in predictive toxicology. The accuracy and reproducibility of toxicity data will be reported in another paper.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherEnvironmental health perspectivesen_US
dc.relation.urihttp://ehp.niehs.nih.gov/en_US
dc.rightsThis article has been published in the journal: Environmental Health Perspectives. Copyright © Environmental Health Perspectives. Used with permission from Environment Health Perspectives.en_US
dc.subjectcomputer scienceen_US
dc.subjectcarcinogenicityen_US
dc.subjectknowledge discoveryen_US
dc.subjectmachine learningen_US
dc.subjectpredictive toxicologyen_US
dc.subjectquality assuranceen_US
dc.subjectstructure-activityen_US
dc.subjectrelationshipsen_US
dc.subjectMachine learning
dc.titleData Quality in Predictive Toxicology: Identification of Chemical Structures and Calculation of Chemical Descriptorsen_US
dc.typeJournal Articleen_US
dc.identifier.doi10.1289/ehp.001081029en_NZ
dc.relation.isPartOfEnvironmental Health Perspectivesen_NZ
pubs.begin-page1029en_NZ
pubs.elements-id28772
pubs.end-page1033en_NZ
pubs.issue11en_NZ
pubs.volume108en_NZ


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