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Volumn 2096, Issue , 2001, Pages 126-135

Mixing a symbolic and a subsymbolic expert to improve carcinogenicity prediction of aromatic compounds

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

EID: 62149089034     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-48219-9_13     Document Type: Conference Paper
Times cited : (7)

References (17)
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    • Benigni, R.1    Richard, A.M.2
  • 4
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    • Learning rules to predict rodent carcinogenicity of non-genotoxic chemicals
    • Lee, Y., Buchanan, B. G., Mattison, D. M., Klopman, G., and Rosenkranz, H. S.: Learning rules to predict rodent carcinogenicity of non-genotoxic chemicals. Mutation Res., vol. 328 (1995) 127-149.
    • (1995) Mutation Res , vol.328 , pp. 127-149
    • Lee, Y.1    Buchanan, B.G.2    Mattison, D.M.3    Klopman, G.4    Rosenkranz, H.S.5
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    • The influence of chemical structure on the extent and sites of carcinogenesis for 522 rodent carcinogens and 55 different human carcinogens exposures
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    • (1993) Mutation Res , vol.286 , pp. 3-74
    • Ashby, J.1    Paton, D.2
  • 7
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    • Overview of Different Artificial Intelligence Approaches Combined with a Deductive Logic-based Expert System for Predicting Chemical Toxicity
    • In Gini and Katritzky, SS-99-01, AAAI Press, Menlo Park, California
    • Darvas, F., Papp, A., Allerdyce, A., Benfenati, E., Tichy, M., Sobb, N., Citti, A., Gini, G.: Overview of Different Artificial Intelligence Approaches Combined with a Deductive Logic-based Expert System for Predicting Chemical Toxicity. In Gini and Katritzky (eds) AAAI Spring Symposium on Predictive Toxicology, SS-99-01, AAAI Press, Menlo Park, California (1999) 94-99.
    • (1999) AAAI Spring Symposium on Predictive Toxicology , pp. 94-99
    • Darvas, F.1    Papp, A.2    Allerdyce, A.3    Benfenati, E.4    Tichy, M.5    Sobb, N.6    Citti, A.7    Gini, G.8
  • 8
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    • Computer prediction of possible toxic action from chemical structure; the DEREK system
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    • Sanderson, D.M.1    Earnshaw, C.G.2
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    • Predictive Carcinogenicity: A Model for Aromatic Compounds, with Nitrogen-containing Substituents, Based on Molecular Descriptors Using an Artificial Neural Network
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.