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Benchmarking variational AutoEncoders on cancer transcriptomics data
Deep generative models, such as variational autoencoders (VAE), have gained increasing attention in computational biology due to their ability to capture complex data manifolds which subsequently can be used to achieve better performance in downstream tasks, such as cancer type prediction or subtypi...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10553230/ https://www.ncbi.nlm.nih.gov/pubmed/37796856 http://dx.doi.org/10.1371/journal.pone.0292126 |