Machine learning classifier approaches for predicting response to RTK-type-III inhibitors demonstrate high accuracy using transcriptomic signatures and ex vivo data

MOTIVATION: The application of machine learning (ML) techniques in the medical field has demonstrated both successes and challenges in the precision medicine era. The ability to accurately classify a subject as a potential responder versus a nonresponder to a given therapy is still an active area of...

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Detalles Bibliográficos
Autores principales: Ferrato, Mauricio H, Marsh, Adam G, Franke, Karl R, Huang, Benjamin J, Kolb, E Anders, DeRyckere, Deborah, Grahm, Douglas K, Chandrasekaran, Sunita, Crowgey, Erin L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10209528/
https://www.ncbi.nlm.nih.gov/pubmed/37250111
http://dx.doi.org/10.1093/bioadv/vbad034

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10209528/
https://www.ncbi.nlm.nih.gov/pubmed/37250111
http://dx.doi.org/10.1093/bioadv/vbad034

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