TUGDA: task uncertainty guided domain adaptation for robust generalization of cancer drug response prediction from in vitro to in vivo settings

MOTIVATION: Large-scale cancer omics studies have highlighted the diversity of patient molecular profiles and the importance of leveraging this information to deliver the right drug to the right patient at the right time. Key challenges in learning predictive models for this include the high-dimensi...

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Detalles Bibliográficos
Autores principales: Peres da Silva, Rafael, Suphavilai, Chayaporn, Nagarajan, Niranjan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Biomedical Informatics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275325/
https://www.ncbi.nlm.nih.gov/pubmed/34000002
http://dx.doi.org/10.1093/bioinformatics/btab299

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275325/
https://www.ncbi.nlm.nih.gov/pubmed/34000002
http://dx.doi.org/10.1093/bioinformatics/btab299

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