| Autor: |
Breitenstein MK; Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA., Hu VJ; School of Medicine, University of California - Irvine, Irvine, CA., Bhatnagar R; Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA., Ratnagiri M; Nemours Pediatric Health System, Wilmington, DE. |
| Abstrakt: |
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 balanced validation (F 1 =0.956) and unbalanced, under-powered testing (F 1 =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. |
