Structure learning of undirected graphical models for count data
Methodology
2020-11-24 v2
Abstract
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.
Cite
@article{arxiv.1810.10854,
title = {Structure learning of undirected graphical models for count data},
author = {Thi Kim Hue Nguyen and Monica Chiogna},
journal= {arXiv preprint arXiv:1810.10854},
year = {2020}
}