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 they can embed complex, noisy high-dimensional gene expression data into a low-dimensional latent space in an unsupervised fashion, enabling us to extract distinguishing features from expression data.

Results

Here, we developed a framework combining a denoising autoencoder and a supervised learning classifier to identify gene signatures related to asthma severity. Using the trained autoencoder with 50 hidden units, we found that hierarchical clustering on the low-dimensional embedding corresp

SUBMITTER: Lou S 

PROVIDER: S-EPMC7560063 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature