gene2gauss: A multi-view gaussian gene embedding learner for analyzing transcriptomic networks.

ABSTRACT: Analyzing gene co-expression networks can help in the discovery of biological processes and regulatory mechanisms underlying normal or perturbed states. Unlike standard differential analysis, network-based approaches consider the interactions between the genes involved leading to biologically relevant results. Applying such network-based methods to jointly analyze multiple transcriptomic networks representing independent disease cohorts or studies could lead to the identification of more robust gene modules or gene regulatory networks. We present gene2gauss, a novel feature learning framework that is capable of embedding genes as multivariate gaussian distributions by taking into account their long-range interaction neighborhoods across multiple transcriptomic studies. Using multiple gene

SUBMITTER: Ghandikota S 

PROVIDER: S-EPMC9285176 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature