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

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

Abstract Background 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 the...

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Bibliographic Details
Main Authors: Shaoke Lou, Tianxiao Li, Daniel Spakowicz, Xiting Yan, Geoffrey Lowell Chupp, Mark Gerstein
Format: Article
Language:English
Published: BMC 2020-10-01
Series:BMC Bioinformatics
Subjects:
Asthma
Asthma subtypes
Denoising autoencoder
Biomarker
Non-invasive
Online Access:http://link.springer.com/article/10.1186/s12859-020-03785-y

Internet

http://link.springer.com/article/10.1186/s12859-020-03785-y

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