Opportunities and obstacles for deep learning in biology and medicine

@article{Ching2017OpportunitiesAO,
  title={Opportunities and obstacles for deep learning in biology and medicine},
  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 Michael Zietz and Michael M. Hoffman 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 Christopher A. Lavender and Srinivas C. Turaga and Amr M. Alexandari and Zhiyong Lu and David J Harris and David DeCaprio and Yanjun Qi and Anshul Kundaje and Yifan Peng and Laura K. Wiley and Marwin H. S. Segler and Simina Maria Boca and S. Joshua Joshua Swamidass and Austin Huang and Anthony Gitter and Casey S. Greene},
  journal={Journal of the Royal Society Interface},
  year={2017},
  volume={15}
}
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. [] Key Result Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress…
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