Prediction of new bioactive molecules using a Bayesian belief network

Journal of Chemical Information and Modeling

Volumn 54, Issue 1, 2014, Pages 30-36

  Abdo, Ammar ^a,c   Leclère, Valérie ^b   Jacques, Philippe ^b   Salim, Naomie ^d   Pupin, Maude ^a  

^a ECOLE CENTRALE DE LILLE   (France)

^b Cité scientifique   (France)

^c HODEIDAH UNIVERSITY   (Yemen)

^d UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; LEARNING ALGORITHMS; MACHINE LEARNING; MOLECULES;

ACTIVITY PREDICTIONS; BIOACTIVE MOLECULES; COMPUTATIONAL CHEMISTS; DIVERSE DATA SETS; INACTIVE COMPOUNDS; NATURAL PRODUCTS; PREDICTION RATE; SYNTHETIC COMPOUNDS;

BAYESIAN NETWORKS;

ALGORITHM; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BIOLOGY; CHEMICAL DATABASE; CHEMICAL MODEL; COMPUTER INTERFACE; DRUG DATABASE; DRUG DEVELOPMENT; DRUG SCREENING; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STATISTICS AND NUMERICAL DATA; VALIDATION STUDY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; COMPUTATIONAL BIOLOGY; DATABASES, CHEMICAL; DATABASES, PHARMACEUTICAL; DRUG DISCOVERY; DRUG EVALUATION, PRECLINICAL; MODELS, CHEMICAL; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; USER-COMPUTER INTERFACE;

EID: 84893365556     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci4004909     Document Type: Article

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