  Personalized medicine remains a major challenge for scientists. The rapid
growth of Machine learning and Deep learning has made them a feasible al-
ternative for predicting the most appropriate therapy for individual patients.
However, the need to develop a custom model for every dataset, the lack of
interpretation of their results and high computational requirements make many
reluctant to use these methods. Aiming to save time and bring light to the way
models work internally, SIBILA has been developed. SIBILA is an ensemble of
machine learning and deep learning models that applies a range of
interpretability algorithms to identify the most relevant input features. Since
the interpretability algo- rithms may not be in line with each other, a
consensus stage has been imple- mented to estimate the global attribution of
each variable to the predictions. SIBILA is containerized to be run on any
high-performance computing plat- form. Although conceived as a command-line
tool, it is also available to all users free of charge as a web server at
https://bio-hpc.ucam.edu/sibila. Thus, even users with few technological skills
can take advantage of it. SIBILA has been applied to two medical case studies
to show its ability to predict in classification problems. Even though it is a
general-purpose tool, it has been developed with the aim of becoming a powerful
decision-making tool for clinicians, but can actually be used in many other
domains. Thus, other two non-medical examples are supplied as supplementary
material to prove that SIBILA still works well with noise and in regression
problems.
