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...

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Detalles Bibliográficos
Autores principales: Eltager, Mostafa, Abdelaal, Tamim, Charrout, Mohammed, Mahfouz, Ahmed, Reinders, Marcel J. T., Makrodimitris, Stavros
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Research Article
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

Internet

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

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