@article {Ching142760, author = {Travers Ching and Daniel S. Himmelstein and Brett K. Beaulieu-Jones and Alexandr A. Kalinin and Brian T. Do and Gregory P. Way and Enrico Ferrero and Paul-Michael Agapow and Wei Xie and Gail L. Rosen and Benjamin J. Lengerich and Johnny Israeli and Jack Lanchantin and Stephen Woloszynek and Anne E. Carpenter and Avanti Shrikumar and Jinbo Xu and Evan M. Cofer and David J. Harris and Dave DeCaprio and Yanjun Qi and Anshul Kundaje and Yifan Peng and Laura K. Wiley and Marwin H.S. Segler and Anthony Gitter and Casey S. Greene}, title = {Opportunities and obstacles for deep learning in biology and medicine}, elocation-id = {142760}, year = {2017}, doi = {10.1101/142760}, publisher = {Cold Spring Harbor Laboratory}, abstract = {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.}, URL = {https://www.biorxiv.org/content/early/2017/05/28/142760}, eprint = {https://www.biorxiv.org/content/early/2017/05/28/142760.full.pdf}, journal = {bioRxiv} }