In parallel to the advances in highthroughput biology, machine learning applications to biomedical data analysis are flourishing.  The integration of multi-omics data analysis and machine learning has led to the discovery of new biomarkers. For example, one of the methods of the [http://mixomics.org/ mixOmics] project implements a method based on sparse Partial Least Squares regression for selection of features (putative biomarkers). A unified and flexible statistical framewok for heterogeneous data integration called "Regularized Generalized Canonical Correlation Analysis" (RGCCA ) enables identifying such putative biomarkers. This framework is implemented and made freely avalaible within the [https://cran.r-project.org/web/packages/RGCCA/ RGCCA R package] .