Statistical model choice including variable selection based on variable importance: A relevant way for biomarkers selection to predict meat tenderness

Marie-Pierre Ellies-Oury (1) , Marie Chavent (2, 3) , Alexandre Conanec (1) , Muriel Bonnet (1) , Brigitte Picard (1) , Jerôme Saracco (3, 2, 4)

1 UMRH - Unité Mixte de Recherche sur les Herbivores - UMR 1213
2 CQFD - Quality control and dynamic reliability
3 IMB - Institut de Mathématiques de Bordeaux
4 ENSC - Ecole Nationale Supérieure de Cognitique

In this paper, we describe a new computational methodology to select the best regression model to predict a numerical variable of interest Y and to select simultaneously the most interesting numerical explanatory variables strongly linked to Y. Three regression models (parametric, semi-parametric and non-parametric) are considered and estimated by multiple linear regression, sliced inverse regression and random forests. Both the variables selection and the model choice are computational. A measure of importance based on random perturbations is calculated for each covariate. The variables above a threshold are selected. Then a learning/test samples approach is used to estimate the Mean Square Error and to determine which model (including variable selection) is the most accurate. The R package modvarsel (MODel and VARiable SELection) implements this computational approach and applies to any regression datasets. After checking the good behavior of the methodology on simulated data, the R package is used to select the proteins predictive of meat tenderness among a pool of 21 candidate proteins assayed in semitendinosus muscle from 71 young bulls. The biomarkers were selected by linear regression (the best regression model) to predict meat tenderness. These biomarkers, we confirm the predominant role of heat shock proteins and metabolic ones.

Domaines: Sciences du Vivant [q-bio]; Statistiques [stat]

Marie-Pierre Ellies-Oury, Marie Chavent, Alexandre Conanec, Muriel Bonnet, Brigitte Picard, et al.. Statistical model choice including variable selection based on variable importance: A relevant way for biomarkers selection to predict meat tenderness. Scientific Reports, 2019, 9 (1), ⟨10.1038/s41598-019-46202-y⟩. ⟨hal-02429345⟩