The estimation of multivariable predictors with good performance in high dimensional settings is a crucial task in biomedical contexts. Usually, solutions based on the application
of a single machine learning model are provided while the use of ensemble methods is often overlooked within this area despite
the well-known benefits that these methods provide in terms of predictive performance. In this paper, four ensemble approaches are described using LASSO base learners to predict the vital status of a patient from RNA-Seq gene expression data. The results of the analysis carried out in a public breast invasive cancer (BRCA) dataset shows that the ensemble approaches outperform statistically significant the standard LASSO model
considered as baseline case. We also perform an analysis of the computational costs involved for each of the approaches,
providing different usage recommendations according to the available computational power.