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Journal of Toxicology and Environmental Health, Part A
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Volume 69, 2006 - Issue 16
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Original Articles

Database Composition Can Affect the Structure–Activity Relationship Prediction

John F. Young Division of Biometry and Risk Assessment, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas, USACorrespondence[email protected]
,
Chen-An Tsai Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
,
James J. Chen Division of Biometry and Risk Assessment, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas, USA
,
John R. Latendresse Toxicologic Pathology Associates, National Center for Toxicological Research, Jefferson, Arkansas, USA
&
Ralph L. Kodell Division of Biometry and Risk Assessment, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas, USA
Pages 1527-1540 | Received 28 Apr 2005, Accepted 28 Jul 2005, Published online: 24 Feb 2007

Citations (3)

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Read on this site (1)

EricaD. Bruce, RobinL. Autenrieth, RobertC. Burghardt, K. C. Donnelly & ThomasJ. McDonald. (2008) Using Quantitative Structure–Activity Relationships (QSAR) to Predict Toxic Endpoints for Polycyclic Aromatic Hydrocarbons (PAH). Journal of Toxicology and Environmental Health, Part A 71:16, pages 1073-1084.
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Articles from other publishers (2)

Ralph L. Kodell, Bruce A. Pearce, Songjoon Baek, Hojin Moon, Hongshik Ahn, John F. Young & James J. Chen. (2009) A model-free ensemble method for class prediction with application to biomedical decision making. Artificial Intelligence in Medicine 46:3, pages 267-276.
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Stephanie Holmgren. 2009. Information Resources in Toxicology. Information Resources in Toxicology 1231 1250 .

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