Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model.

ABSTRACT:

Background

A living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly controlled transcriptional program. An important and yet challenging task in systems biology is to reconstruct cellular signaling system in a data-driven manner. In this study, we investigate the utility of deep hierarchical neural networks in learning and representing the hierarchical organization of yeast transcriptomic machinery.

Results

We have designed a sparse autoencoder model consisting of a layer of observed variables and four layers of hidden variables. We applied the model to over a thousand of yeast microarrays to learn t

SUBMITTER: Chen L 

PROVIDER: S-EPMC4895523 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature