Graphical Modelling in Genetics and Systems Biology
This work tackles the curse of dimensionality and modelling difficulties in genetics and systems biology, but it appears incremental as it reviews existing challenges and solutions without introducing new methods.
The paper addresses the challenges of applying graphical modelling to genetics and systems biology, where high-dimensional data with few observations cause computational and statistical issues, and it discusses potential solutions to these problems.
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.