Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs).

Belfield, Samuel J; Cronin, Mark T D; Enoch, Steven J; Firman, James W

Belfield, Samuel J; Cronin, Mark T D; Enoch, Steven J; Firman, James W.

Affiliation

Belfield SJ; School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United Kingdom.
Cronin MTD; School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United Kingdom.
Enoch SJ; School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United Kingdom.
Firman JW; School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United Kingdom.

PLoS One ; 18(5): e0282924, 2023.

Article de En | MEDLINE | ID: mdl-37163504

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Algorithmes / Relation quantitative structure-activité Type d'étude: Guideline / Prognostic_studies Limites: Animals Langue: En Journal: PLoS One Sujet du journal: CIENCIA / MEDICINA Année: 2023 Type de document: Article Pays d'affiliation: Royaume-Uni

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