Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks.

ABSTRACT: Cross-experiment comparisons in public data compendia are challenged by unmatched conditions and technical noise. The ADAGE method, which performs unsupervised integration with denoising autoencoder neural networks, can identify biological patterns, but because ADAGE models, like many neural networks, are over-parameterized, different ADAGE models perform equally well. To enhance model robustness and better build signatures consistent with biological pathways, we developed an ensemble ADAGE (eADAGE) that integrated stable signatures across models. We applied eADAGE to a compendium of Pseudomonas aeruginosa gene expression profiling experiments performed in 78 media. eADAGE revealed a phosphate starvation response controlled by PhoB in media with moderate phosphate and predicted that a seco

SUBMITTER: Tan J 

PROVIDER: S-EPMC5532071 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature