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

ABSTRACT: Systemic lupus erythematosus (SLE) is a rare, autoimmune disorder known to affect most organ sites. Complicating clinical management is a poorly differentiated, heterogenous SLE disease state. While some small molecule drugs and biologics are available for treatment, additional therapeutic options are needed. Parsing complex biological signatures using powerful, yet human interpretable approaches is critical to advancing our understanding of SLE etiology and identifying therapeutic repositioning opportunities. To approach this goal, we developed a semi-supervised deep neural network pipeline for gene expression profiling of SLE patients and subsequent characterization of individual gene features. Our pipeline performed exemplar multinomial classification of SLE patients in independent bala

SUBMITTER: Breitenstein MK 

PROVIDER: S-EPMC6568105 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature