  Rapid advances of hardware-based technologies during the past decades have
opened up new possibilities for Life scientists to gather multimodal data in
various application domains (e.g., Omics, Bioimaging, Medical Imaging, and
[Brain/Body]-Machine Interfaces), thus generating novel opportunities for
development of dedicated data intensive machine learning techniques. Overall,
recent research in Deep learning (DL), Reinforcement learning (RL), and their
combination (Deep RL) promise to revolutionize Artificial Intelligence. The
growth in computational power accompanied by faster and increased data storage
and declining computing costs have already allowed scientists in various fields
to apply these techniques on datasets that were previously intractable for
their size and complexity. This review article provides a comprehensive survey
on the application of DL, RL, and Deep RL techniques in mining Biological data.
In addition, we compare performances of DL techniques when applied to different
datasets across various application domains. Finally, we outline open issues in
this challenging research area and discuss future development perspectives.
