  Many of the current scientific advances in the life sciences have their
origin in the intensive use of data for knowledge discovery. In no area this is
so clear as in bioinformatics, led by technological breakthroughs in data
acquisition technologies. It has been argued that bioinformatics could quickly
become the field of research generating the largest data repositories, beating
other data-intensive areas such as high-energy physics or astroinformatics.
Over the last decade, deep learning has become a disruptive advance in machine
learning, giving new live to the long-standing connectionist paradigm in
artificial intelligence. Deep learning methods are ideally suited to
large-scale data and, therefore, they should be ideally suited to knowledge
discovery in bioinformatics and biomedicine at large. In this brief paper, we
review key aspects of the application of deep learning in bioinformatics and
medicine, drawing from the themes covered by the contributions to an ESANN 2018
special session devoted to this topic.
