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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Oxford University Press
2023
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| 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 |