Graphical Modelling in Genetics and Systems Biology

TL;DR

This paper reviews the application of graphical modelling in genetics and systems biology, highlighting challenges like high dimensionality and limited data, and discusses potential solutions to improve modelling accuracy.

Contribution

It provides an overview of the unique challenges in applying graphical models to large biological datasets and explores strategies to address computational and statistical issues.

Findings

High dimensionality complicates graphical model inference in biology.

Limited observations hinder statistical significance of networks.

Potential solutions include dimensionality reduction and specialized algorithms.

Abstract

Graphical modelling has a long history in statistics as a tool for the analysis of multivariate data, starting from Wright's path analysis and Gibbs' applications to statistical physics at the beginning of the last century. In its modern form, it was pioneered by Lauritzen and Wermuth and Pearl in the 1980s, and has since found applications in fields as diverse as bioinformatics, customer satisfaction surveys and weather forecasts. Genetics and systems biology are unique among these fields in the dimension of the data sets they study, which often contain several hundreds of variables and only a few tens or hundreds of observations. This raises problems in both computational complexity and the statistical significance of the resulting networks, collectively known as the "curse of dimensionality". Furthermore, the data themselves are difficult to model correctly due to the limited understanding of the underlying mechanisms. In the following, we will illustrate how such challenges affect practical graphical modelling and some possible solutions.