Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models.

ABSTRACT: Background: Biological entities such as genes, promoters, mRNA, metabolites or proteins do not act alone, but in concert in their network context. Modules, i.e., groups of nodes with similar topological properties in these networks characterize important biological functions of the underlying biomolecular system. Edges in such molecular networks represent regulatory and physical interactions, and comparing them between conditions provides valuable information on differential molecular mechanisms. However, biological data is inherently noisy and network reduction techniques can propagate errors particularly to the level of edges. We aim to improve the analysis of networks of biological molecules by deriving modules together with edge relevance estimations that are based on global network ch

SUBMITTER: Baum K 

PROVIDER: S-EPMC6743255 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature