Novel uses of in vitro data to develop quantitative biological activity relationship models for in vivo carcinogenicity prediction

Molecular Informatics

Volumn 34, Issue 4, 2015, Pages 236-245

  Pradeep, Prachi ^a   Povinelli, Richard J ^b   Merrill, Stephen J ^a   Bozdag, Serdar ^a   Sem, Daniel S ^c  

^a MARQUETTE UNIVERSITY   (United States)

^b Marquette University   (United States)

^c Department of Pharmaceutical Sciences   (United States)

Author keywords

Biological descriptors; Carcinogenicity; Computational toxicology; In vitro data; OECD; Quantitative biological activity relationship (QBAR); REACH

Indexed keywords

ASSAYS; BIOINFORMATICS; COMPUTATIONAL CHEMISTRY; FORECASTING; LEARNING ALGORITHMS; LEARNING SYSTEMS; MOLECULAR GRAPHICS; TOXICITY;

BIOLOGICAL DESCRIPTOR; CARCINOGENICITY; COMPUTATIONAL TOXICOLOGY; DESCRIPTORS; IN VITRO DATA; IN-VITRO; OECD; QUANTITATIVE BIOLOGICAL ACTIVITY RELATIONSHIP; REACH; RELATIONSHIP MODEL;

BIOACTIVITY;

CYTOCHROME P450; CARCINOGEN;

ACCURACY; ALGORITHM; ARTICLE; BIOLOGICAL ACTIVITY; CARCINOGENICITY; CASE STUDY; COMPUTER MODEL; ENZYME ACTIVITY; ENZYME INDUCTION; IN VITRO STUDY; IN VIVO STUDY; MACHINE LEARNING; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; QUANTITATIVE BIOLOGICAL ACTIVITY RELATIONSHIP; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; BIOLOGICAL MODEL; CHEMISTRY; FACTUAL DATABASE;

CARCINOGENS; DATABASES, FACTUAL; MACHINE LEARNING; MODELS, BIOLOGICAL;

EID: 84926429047     PISSN: 18681743     EISSN: 18681751     Source Type: Journal    
DOI: 10.1002/minf.201400168     Document Type: Article

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