Network depth affects inference of gene sets from bacterial transcriptomes using denoising autoencoders.

ABSTRACT:

Summary

The increasing number of publicly available bacterial gene expression data sets provides an unprecedented resource for the study of gene regulation in diverse conditions, but emphasizes the need for self-supervised methods for the automated generation of new hypotheses. One approach for inferring coordinated regulation from bacterial expression data is through neural networks known as denoising autoencoders (DAEs) which encode large datasets in a reduced bottleneck layer. We have generalized this application of DAEs to include deep networks and explore the effects of network architecture on gene set inference using deep learning. We developed a DAE-based pipeline to extract gene sets from transcriptomic data in Escherichia coli, validate our method by comparing infer

SUBMITTER: Kion-Crosby W 

PROVIDER: S-EPMC11256956 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature