Deep Learning in the BioMedical Sciences

Pierre Baldi

University of California, Irvine, USA

Abstract

We will provide a brief historical overview of deep learning and its two-way interactions with biology. In one direction, progress in deep learning has been driven by progress in neuroscience, from the early beginnings of deep learning to the most recent theories of local learning and the role of gating in attention. In the other direction, there have been many applications of deep learning in the biomedical sciences, some of which have also driven progress in deep learning, including the development of convolutional neural networks for fingerprint recognition and of graph/recursive neural networks in protein structure prediction and other molecular applications. We will review such applications from our research group across multiple spatial and temporal scales, including recent applications in biomedical imaging and discuss the interpretability problem and future directions.