Improved survival analysis by learning shared genomic information from pan-cancer data

MOTIVATION: Recent advances in deep learning have offered solutions to many biomedical tasks. However, there remains a challenge in applying deep learning to survival analysis using human cancer transcriptome data. As the number of genes, the input variables of survival model, is larger than the amo...

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Detalles Bibliográficos
Autores principales: Kim, Sunkyu, Kim, Keonwoo, Choe, Junseok, Lee, Inggeol, Kang, Jaewoo
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/PMC7355236/
https://www.ncbi.nlm.nih.gov/pubmed/32657401
http://dx.doi.org/10.1093/bioinformatics/btaa462

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355236/
https://www.ncbi.nlm.nih.gov/pubmed/32657401
http://dx.doi.org/10.1093/bioinformatics/btaa462

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