Biologically informed variational autoencoders allow predictive modeling of genetic and drug-induced perturbations

MOTIVATION: Variational autoencoders (VAEs) have rapidly increased in popularity in biological applications and have already successfully been used on many omic datasets. Their latent space provides a low-dimensional representation of input data, and VAEs have been applied, e.g. for clustering of si...

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Detalles Bibliográficos
Autores principales: Doncevic, Daria, Herrmann, Carl
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301695/
https://www.ncbi.nlm.nih.gov/pubmed/37326971
http://dx.doi.org/10.1093/bioinformatics/btad387

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301695/
https://www.ncbi.nlm.nih.gov/pubmed/37326971
http://dx.doi.org/10.1093/bioinformatics/btad387

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