Protein co-expression network analysis (ProCoNA).

ABSTRACT:

Background

Biological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology.

Results

We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a stati

SUBMITTER: Gibbs DL 

PROVIDER: S-EPMC3695838 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature