Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility

Toxicological Sciences

Volumn 165, Issue 1, 2018, Pages 198-212

  Luechtefeld, Thomas ^a,b   Marsh, Dan ^b   Rowlands, Craig ^c   Hartung, Thomas ^a,d  

^a JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

^b ToxTrack   (United States)

^c UNDERWRITERS LABORATORIES INC   (United States)

^d UNIVERSITY OF KONSTANZ   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; EYE INJURY; HEALTH HAZARD; HUMAN; MACHINE LEARNING; MUTAGENICITY; NATURAL LANGUAGE PROCESSING; PRACTICE GUIDELINE; REFERENCE DATABASE; SKIN IRRITATION; SKIN SENSITIZATION; SKIN TOXICITY; STRUCTURE ACTIVITY RELATION; TOXICOLOGY; ANIMAL; ANIMAL TESTING ALTERNATIVE; CHEMICAL DATABASE; CHEMISTRY; DANGEROUS GOODS; DATA MINING; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THEORETICAL MODEL; TOXICITY;

ANIMAL TESTING ALTERNATIVES; ANIMALS; BIG DATA; DATA MINING; DATABASES, CHEMICAL; HAZARDOUS SUBSTANCES; HUMANS; MACHINE LEARNING; MODELS, THEORETICAL; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 85054881735     PISSN: 10966080     EISSN: 10960929     Source Type: Journal    
DOI: 10.1093/toxsci/kfy152     Document Type: Article

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