  Background: Understanding the relationship between the Omics and the
phenotype is a central problem in precision medicine. The high dimensionality
of metabolomics data challenges learning algorithms in terms of scalability and
generalization. Most learning algorithms do not produce interpretable models --
Method: We propose an ensemble learning algorithm based on conjunctions or
disjunctions of decision rules. -- Results : Applications on metabolomics data
shows that it produces models that achieves high predictive performances. The
interpretability of the models makes them useful for biomarker discovery and
patterns discovery in high dimensional data.
