RT Journal Article
SR Electronic
T1 Opportunities and obstacles for deep learning in biology and medicine
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 142760
DO 10.1101/142760
A1 Travers Ching
A1 Daniel S. Himmelstein
A1 Brett K. Beaulieu-Jones
A1 Alexandr A. Kalinin
A1 Brian T. Do
A1 Gregory P. Way
A1 Enrico Ferrero
A1 Paul-Michael Agapow
A1 Wei Xie
A1 Gail L. Rosen
A1 Benjamin J. Lengerich
A1 Johnny Israeli
A1 Jack Lanchantin
A1 Stephen Woloszynek
A1 Anne E. Carpenter
A1 Avanti Shrikumar
A1 Jinbo Xu
A1 Evan M. Cofer
A1 David J. Harris
A1 Dave DeCaprio
A1 Yanjun Qi
A1 Anshul Kundaje
A1 Yifan Peng
A1 Laura K. Wiley
A1 Marwin H.S. Segler
A1 Anthony Gitter
A1 Casey S. Greene
YR 2017
UL http://biorxiv.org/content/early/2017/05/28/142760.abstract
AB Deep learning, which describes a class of machine learning algorithms, has recently showed impressive results across a variety of domains. Biology and medicine are data rich, but the data are complex and often ill-understood. Problems of this nature may be particularly well-suited to deep learning techniques. We examine applications of deep learning to a variety of biomedical problems -- patient classification, fundamental biological processes, and treatment of patients -- to predict whether deep learning will transform these tasks or if the biomedical sphere poses unique challenges. We find that deep learning has yet to revolutionize or definitively resolve any of these problems, but promising advances have been made on the prior state of the art. Even when improvement over a previous baseline has been modest, we have seen signs that deep learning methods may speed or aid human investigation. More work is needed to address concerns related to interpretability and how to best model each problem. Furthermore, the limited amount of labeled data for training presents problems in some domains, as can legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning powering changes at the bench and bedside with the potential to transform several areas of biology and medicine.