Article

XA4C: eXplainable representation learning via Autoencoders revealing Critical genes

2023-07-17

Abstract excerpt

<h4>ABSTRACT</h4> Machine Learning models have been frequently used in transcriptome analyses. Particularly, Representation Learning (RL), e.g., autoencoders, are effective in learning critical representations in noisy data. However, learned representations, e.g., the “latent variables” in an autoencoder, are difficult to interpret, not to mention prioritizing essential genes for functional follow-up. In contrast...

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Literature Corpus work: 2fa28b75-2aab-5523-a581-017c6eaa5f54
DOI: 10.1101/2023.07.16.549209

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