A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology.

We propose a new methodology for selecting and ranking covariates associated with a variable of interest in a context of high-dimensional data under dependence but few observations. The methodology successively intertwines the clustering of covariates, decorrelation of covariates using Factor Latent Analysis, selection using aggregation of adapted methods and finally ranking. A simulation study shows the interest of the decorrelation inside the different clusters of covariates.

Nested structure of intraspecific competition network in Carnobacterium maltaromaticum.

While competition targeting food-borne pathogens is being widely documented, few studies have focused on competition among non-pathogenic food bacteria. Carnobacterium maltaromaticum is a genetically diverse lactic acid bacterium known for comprising several bacteriocinogenic strains with bioprotective potentialities against the food-borne pathogen Listeria monocytogenes. The aim of our study is to examine the network properties of competition among a collection of 73 strains of C.