10 Eadage Models For The Pao1 Kegg Pathways Case Study In Pathcore

ensemble Analysis using Denoising Autoencoders for Gene Expression<strong> </strong>(<strong>eADAGE</strong>) is an unsupervised feature construction algorithm developed by Tan et al. that uses an ensemble of neural networks (an ensemble of ADAGE models) to capture biological signatures embedded in the expression compendium. By initializing eADAGE with different random seeds, Tan et al. produced 10 eADAGE models that each extracted k=300 features from the compendium of genome-scale <em>P. aeruginosa</em> data. eADAGE is described in Tan et al.'s "System-wide automatic extraction of functional signatures in <em>Pseudomonas aeruginosa</em> with eADAGE" (https://doi.org/10.1101/078659). The code to construct these 10 models is available in this repository: https://bitbucket.org/greenelab/eadage (see eADAGE_construction.sh).