MARS as an alternative approach of Gaussian graphical model for biochemical networks

The Gaussian graphical model (GGM) is one of the well-known modelling approaches to describe biological networks under the steady-state condition via the precision matrix of data. In literature there are different methods to infer model parameters based on GGM. The neighbourhood selection with the lasso regression and the graphical lasso method are the most common techniques among these alternative estimation methods. But they can be computationally demanding when the system's dimension increases. Here, we suggest a non-parametric statistical approach, called the multivariate adaptive regression splines (MARS) as an alternative of GGM. To compare the performance of both models, we evaluate the findings of normal and non-normal data via the specificity, precision, F-measures and their computational costs. From the outputs, we see that MARS performs well, resulting in, a plausible alternative approach with respect to GGM in the construction of complex biological systems.