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

Abstract 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 clusteri...

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Veröffentlicht in:	Bioinformatics (Oxford, England) England), 2023-06, Vol.39 (6)
Hauptverfasser:	Doncevic, Daria, Herrmann, Carl
Format:	Artikel
Sprache:	eng
Schlagworte:	Cluster Analysis Gene Expression Profiling Original Paper Transcriptome
Online-Zugang:	Volltext
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