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

Environmental Health Perspectives

Volumn 104, Issue SUPPL. 5, 1996, Pages 1031-1040

  King, Ross D ^a   Srinivasan, Ashwin ^b  

^a IMPERIAL CANCER RESEARCH FUND   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Artificial intelligence; Machine learning; National Toxicology Program; SAR

Indexed keywords

AROMATIC NITRO COMPOUND; SURAMIN;

ACCURACY; ALGORITHM; ANGIOGENESIS; ARTIFICIAL INTELLIGENCE; BIOASSAY; CARCINOGENICITY; CHEMICAL STRUCTURE; COMPUTER PROGRAM; CONFERENCE PAPER; DATA BASE; LEARNING; LOGIC; MUTAGENICITY; PREDICTION; PRIORITY JOURNAL; RODENT; SALMONELLA; STRUCTURE ACTIVITY RELATION;

RODENTIA; SALMONELLA;

EID: 0029824974     PISSN: 00916765     EISSN: None     Source Type: Journal    
DOI: 10.1289/ehp.96104s51031     Document Type: Conference Paper

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