A multi-omics supervised autoencoder for pan-cancer clinical outcome endpoints prediction

BACKGROUND: With the rapid development of sequencing technologies, collecting diverse types of cancer omics data become more cost-effective. Many computational methods attempted to represent and fuse multiple omics into a comprehensive view of cancer. However, different types of omics are related an...

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Detalles Bibliográficos
Autores principales: Tan, Kaiwen, Huang, Weixian, Hu, Jinlong, Dong, Shoubin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7477832/
https://www.ncbi.nlm.nih.gov/pubmed/32646413
http://dx.doi.org/10.1186/s12911-020-1114-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7477832/
https://www.ncbi.nlm.nih.gov/pubmed/32646413
http://dx.doi.org/10.1186/s12911-020-1114-3

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