Work

Approaching neural net feature interpretation using stacked autoencoders: gene expression profiling of systemic lupus erythematosus patients.

Year: 2019

Type: article

Abstract: =0.944) cohorts. A stacked autoencoder disambiguated individual feature representativeness by regenerating an input-like(A ') feature matrix. A to A' comparisons suggest the top associated features to be key features in gene expression profiling using neural nets.

Source: PubMed

Authors: Matthew K Breitenstein, Vincent Jy Hu, Roopal Bhatnagar, Madhavi Ratnagiri

Institutions: University of Pennsylvania, University of California, Irvine

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W2995289182

FWCI: 0.146

Cites: 18

Cited by: 3

Related to: 10