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XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data
The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This ‘black box’ problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we present XOmiVAE, a variational autoencoder (VAE)-bas...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8575033/ https://www.ncbi.nlm.nih.gov/pubmed/34402865 http://dx.doi.org/10.1093/bib/bbab315 |