Literature DB >> 7739598

Learning rules to predict rodent carcinogenicity of non-genotoxic chemicals.

Y Lee1, B G Buchanan, D M Mattison, G Klopman, H S Rosenkranz.   

Abstract

The results of short-term assays (induction of chromosomal aberrations and sister-chromatid exchanges, oncogenic transformations and cellular toxicity) together with MTD (maximum tolerated dose) values and physical chemical properties of non-genotoxic (i.e. Salmonella non-mutagens) carcinogens and non-carcinogens were submitted to RL, an inductive learning program. RL was able to learn rules that correctly predicted between 70 and 80% of non-genotoxic chemicals. This is a marked improvement over current predictions using only the results of short-term assays and exceeds the predictions of human experts that used the whole spectrum of acute and subchronic toxicity results as well as human knowledge and intuition.

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Year:  1995        PMID: 7739598     DOI: 10.1016/0027-5107(94)00202-g

Source DB:  PubMed          Journal:  Mutat Res        ISSN: 0027-5107            Impact factor:   2.433


  6 in total

1.  Global structure-activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors.

Authors:  Albert R Cunningham; C Alex Carrasquer; Shahid Qamar; Jon M Maguire; Suzanne L Cunningham; John O Trent
Journal:  Carcinogenesis       Date:  2012-06-07       Impact factor: 4.944

2.  The discovery of indicator variables for QSAR using inductive logic programming.

Authors:  R D King; A Srinivasan
Journal:  J Comput Aided Mol Des       Date:  1997-11       Impact factor: 3.686

3.  Transfer learning of classification rules for biomarker discovery and verification from molecular profiling studies.

Authors:  Philip Ganchev; David Malehorn; William L Bigbee; Vanathi Gopalakrishnan
Journal:  J Biomed Inform       Date:  2011-05-06       Impact factor: 6.317

4.  Carcinogenicity predictions for a group of 30 chemicals undergoing rodent cancer bioassays based on rules derived from subchronic organ toxicities.

Authors:  Y Lee; B G Buchanan; H S Rosenkranz
Journal:  Environ Health Perspect       Date:  1996-10       Impact factor: 9.031

5.  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

6.  ANN-Based Integrated Risk Ranking Approach: A Case Study of Contaminants of Emerging Concern of Fish and Seafood in Europe.

Authors:  Vikas Kumar; Saurav Kumar
Journal:  Int J Environ Res Public Health       Date:  2021-02-08       Impact factor: 3.390

  6 in total

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