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

ABSTRACT

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.

KEYWORDS:

• Deterministic inference
• multivariate adaptive regression splines
• optimal model selection
• Monte Carlo simulations
• systems biology

AMS SUBJECT CLASSIFICATION:

• 01A23
• 45B67

Acknowledgments

Furthermore, the author thank to the editor and anonymous referees of this article for their valuable contributions which significantly improve the quality of the study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by EU 7. Framework Colloborative project [project number 260429] and by the Scientific and Technological Research Council of Turkey [project number 114E636].