AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics

MOTIVATION: The goal of pharmacogenomics is to predict drug response in patients using their single- or multi-omics data. A major challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clin...

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Detalles Bibliográficos
Autores principales: Sharifi-Noghabi, Hossein, Peng, Shuman, Zolotareva, Olga, Collins, Colin C, Ester, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Studies of Phenotypes and Clinical Applications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355265/
https://www.ncbi.nlm.nih.gov/pubmed/32657371
http://dx.doi.org/10.1093/bioinformatics/btaa442

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355265/
https://www.ncbi.nlm.nih.gov/pubmed/32657371
http://dx.doi.org/10.1093/bioinformatics/btaa442

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