Probabilistic Graphical Models Applied to Biological Networks.

Biological networks can be defined as a set of molecules and all the interactions among them. Their study can be useful to predict gene function, phenotypes, and regulate molecular patterns. Probabilistic graphical models (PGMs) are being widely used to integrate different data sources with modeled biological networks. The inference of these models applied to large-scale experiments of molecular biology allows us to predict influences of the experimental treatments in the behavior/phenotype of organisms. Here, we introduce the main types of PGMs and their applications in a biological networks context.