Article

A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data

2023-05-24

Abstract excerpt

Interpreting transcriptome data is an important yet challenging aspect of bioinformatic analysis. While gene set enrichment analysis is a standard tool for interpreting regulatory changes, we utilize deep learning techniques, specifically autoencoder architectures, to learn latent variables that drive transcriptome signals. We investigate whether simple, variational autoencoder (VAE), and beta-weighted VAE are cap...

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Literature Corpus work: 22a9c0f7-dad9-54bc-89c1-2063d5723ddf
DOI: 10.1101/2023.05.22.541678

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