  Biological processes underlying the basic functions of a cell involve complex
interactions between genes. From a technical point of view, these interactions
can be represented through a graph where genes and their connections are,
respectively, nodes and edges. The main objective of this paper is to develop a
statistical framework for modelling the interactions between genes when the
activity of genes is measured on a discrete scale. In detail, we define a new
algorithm for learning the structure of undirected graphs, PC-LPGM, proving its
theoretical consistence in the limit of infinite observations. The proposed
algorithm shows promising results when applied to simulated data as well as to
real data.
