BioinformaticsPLoS Computational BiologyPeer-reviewed
A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data
Bin Liu, Bodo Rosenhahn, Thomas Illig, David S. DeLuca
This study introduces a prior-informed VAE for transcriptome data that improves interpretability by learning latent features aligned with biological pathways.
4 citations