Applying interpretable machine learning workflow to evaluate exposure–response relationships for large‐molecule oncology drugs

The application of logistic regression (LR) and Cox Proportional Hazard (CoxPH) models are well‐established for evaluating exposure–response (E–R) relationship in large molecule oncology drugs. However, applying machine learning (ML) models on evaluating E–R relationships has not been widely explore...

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Detalles Bibliográficos
Autores principales: Liu, Gengbo, Lu, James, Lim, Hong Seo, Jin, Jin Yan, Lu, Dan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755920/
https://www.ncbi.nlm.nih.gov/pubmed/36193885
http://dx.doi.org/10.1002/psp4.12871

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755920/
https://www.ncbi.nlm.nih.gov/pubmed/36193885
http://dx.doi.org/10.1002/psp4.12871

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