Article

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

2020-09-28

Abstract excerpt

<h4>Background: </h4> 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. Potentially, autoencoders provide ideal frameworks for such tasks as they can embed complex, noisy high-dimensional gene expression data into a low-dimensional latent space in an unsuperv...

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Literature Corpus work: ebf76ee0-9b4d-50d3-82b7-cc07e17937ca
DOI: 10.21203/rs.2.21701/v4

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