Literature DB >> 2263200

Prediction of the carcinogenicity in rodents of chemicals currently being tested by the US National Toxicology Program: structure-activity correlations.

H S Rosenkranz1, G Klopman.   

Abstract

CASE, an artificial intelligence structure-activity relational system, was used to predict the carcinogenicity of a group of chemicals currently being tested in rodent bioassays by the US National Toxicology Program. The 'learning set' for the CASE predictions consisted of the results of previous 252 rodent carcinogenicity bioassays.

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Year:  1990        PMID: 2263200     DOI: 10.1093/mutage/5.5.425

Source DB:  PubMed          Journal:  Mutagenesis        ISSN: 0267-8357            Impact factor:   3.000


  4 in total

1.  Prediction of rodent carcinogenicity bioassays from molecular structure using inductive logic programming.

Authors:  R D King; A Srinivasan
Journal:  Environ Health Perspect       Date:  1996-10       Impact factor: 9.031

2.  Predicting chemical carcinogenesis in rodents.

Authors:  J T Wachsman; D W Bristol; J Spalding; M Shelby; R W Tennant
Journal:  Environ Health Perspect       Date:  1993-10       Impact factor: 9.031

3.  Data selection and treatment of chemicals tested for genotoxicity and carcinogenicity.

Authors:  N Loprieno; G Boncristiani; G Loprieno; M Tesoro
Journal:  Environ Health Perspect       Date:  1991-12       Impact factor: 9.031

Review 4.  Risk and benefit evaluation in development of pharmaceutical products.

Authors:  C S Aaron; P R Harbach; S S Mattano; J K Mayo; Y Wang; R L Yu; D M Zimmer
Journal:  Environ Health Perspect       Date:  1993-10       Impact factor: 9.031

  4 in total

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