  In recent years, several machine learning approaches have been proposed to
predict gene expression and epigenetic signals from the DNA sequence alone.
These models are often used to deduce, and, to some extent, assess putative new
biological insights about gene regulation, and they have led to very
interesting advances in regulatory genomics. This article reviews a selection
of these methods, ranging from linear models to random forests, kernel methods,
and more advanced deep learning models. Specifically, we detail the different
techniques and strategies that can be used to extract new gene-regulation
hypotheses from these models. Furthermore, because these putative insights need
to be validated with wet-lab experiments, we emphasize that it is important to
have a measure of confidence associated with the extracted hypotheses. We
review the procedures that have been proposed to measure this confidence for
the different types of machine learning models, and we discuss the fact that
they do not provide the same kind of information.
