eXtasy simplified-towards opening the black box

Exome sequencing remarkably simplifies the search for mutations causing rare monogenic disorders. Still, due to a big number of potential candidate variants, computational methods are needed to facilitate this process. Recently, an algorithm based on genomic data fusion has been proposed in this context (eXtasy), which exhibits highly competitive performances among the state of the art methods. Nonetheless, being based on a Random Forest classifier, its core model is characterized by a prohibitive size, slow execution speed and difficulties associated with gaining insights in the decision-making process. Here we propose a simplification of the original eXtasy algorithm that retains superior ranking capability of former without suffering from the both high complexity and low interpretability.