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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2020
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| Materias: | |
| 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 |