Representing core gene expression activity relationships using the latent structure implicit in Bayesian networks.

ABSTRACT:

Motivation

Many types of networks, such as co-expression or ChIP-seq-based gene-regulatory networks, provide useful information for biomedical studies. However, they are often too full of connections and difficult to interpret, forming "indecipherable hairballs."

Results

To address this issue, we propose that a Bayesian network can summarize the core relationships between gene expression activities. This network, which we call the LatentDAG, is substantially simpler than conventional co-expression network and ChIP-seq networks (by two orders of magnitude). It provides clearer clusters, without extraneous cross-cluster connections, and clear separators between modules. Moreover, one can find a number of clear examples showing how it bridges the connection between steps in the

SUBMITTER: Gao J 

PROVIDER: S-EPMC11316617 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature