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Machine Learning Approaches in Computational Toxicology Studies
Book PartAbstract: In the present world, the use of computational toxicity assessment techniques is highly encouraged aPalabras claves:classification, Clustering, computational toxicity, Decision Trees, deep learning, Dimensionality reduction, hierarchical clustering, K-Means, K-nearest neighbor, linear discriminant analysis, linear regression, logistic regression, Machine learning, Naïve Bayes, neural network, Principal Component Analysis, QSAR, random forest, regression, semi-supervised, Supervised, SUPPORT VECTOR MACHINES, unsupervisedAutores:Ambure P., Gozalbes R., Stephen Jones BarigyeFuentes:scopusPbkp_redictive modeling of aryl hydrocarbon receptor (AhR) agonism
ArticleAbstract: The aryl hydrocarbon receptor (AhR) plays a key role in the regulation of gene expression in metabolPalabras claves:Agonistic activity, Aryl hydrocarbon receptor, Benzothiazoles, Computational modeling, Coumarins, flavonoids, Polyphenols, QSAR, TriterpenesAutores:Giner R.M., Gozalbes R., Jorge E.G., Stephen Jones Barigye, Veitía Iyarreta M.Fuentes:scopus