Article

Latent-space embedding of expression data identifies gene signatures from sputum samples of asthmatic patients

2019-05-22

Abstract excerpt

The pathogenesis of asthma is a complex process involving multiple genes and pathways. Identifying biomarkers from asthma datasets, especially those that include heterogeneous subpopulations, is challenging. In this work, we developed a framework that incorporates a denoising autoencoder and a supervised learning approach to identify gene signatures related to asthma severity. The autoencoder embeds high-dimension...

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Literature Corpus work: 10970a00-f68a-545e-aa34-494e5ffba29a
DOI: 10.1101/646976

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