Machine learning-guided, big data-enabled, biomarker-based systems pharmacology: modeling the stochasticity of natural history and disease progression.

McComb, Mason; Blair, Rachael Hageman; Lysy, Martin; Ramanathan, Murali

McComb, Mason; Blair, Rachael Hageman; Lysy, Martin; Ramanathan, Murali.

Afiliação

McComb M; Department of Pharmaceutical Sciences, University at Buffalo, The State University of New York, 355 Pharmacy Building, Buffalo, NY, 14214-8033, USA.
Blair RH; Department of Biostatistics, University at Buffalo, The State University of New York, Buffalo, NY, USA.
Lysy M; Department of Biostatistics, University of Waterloo, Waterloo, ON, Canada.
Ramanathan M; Department of Pharmaceutical Sciences, University at Buffalo, The State University of New York, 355 Pharmacy Building, Buffalo, NY, 14214-8033, USA. Murali@Buffalo.Edu.

J Pharmacokinet Pharmacodyn ; 49(1): 65-79, 2022 02.

Article em En | MEDLINE | ID: mdl-34611796

Assuntos

Big Data; Farmacologia em Rede; Teorema de Bayes; Biomarcadores; Progressão da Doença; Humanos; Aprendizado de Máquina; Modelos Biológicos; Inquéritos Nutricionais

Palavras-chave

AI; Artificial intelligence; Bayesian networks; Disease biomarkers; Machine learning; Pharmacometrics; Stochastic modeling

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Big Data / Farmacologia em Rede Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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