bioRxiv preprint doi: https://doi.org/10.1101/142760; this version posted January 19, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Opportunities and obstacles for deep learning in biology and medicine A DOI-citable preprint of this manuscript is available at https://doi.org/10.1101/142760. This manuscript was automatically generated from greenelab/deep-review@a01dd71 on January 19, 2018. Authors 1,☯ 2 3 4 Travers Ching , Daniel S. Himmelstein , Brett K. Beaulieu-Jones , Alexandr A. Kalinin , 5 2 6 7 2 Brian T. Do , Gregory P. Way , Enrico Ferrero , Paul-Michael Agapow , Michael Zietz , 8,9,10 11 12 13 Michael M. Hoffman , Wei Xie , Gail L. Rosen , Benjamin J. Lengerich , Johnny 14 15 12 16 Israeli , Jack Lanchantin , Stephen Woloszynek , Anne E. Carpenter , Avanti 17 18 19,20 21 Shrikumar , Jinbo Xu , Evan M. Cofer , Christopher A. Lavender , Srinivas C. 22 17 23 24 25 Turaga , Amr M. Alexandari , Zhiyong Lu , David J. Harris , Dave DeCaprio , 15 17,26 23 27 28 Yanjun Qi , Anshul Kundaje , Yifan Peng , Laura K. Wiley , Marwin H.S. Segler , 29 30 31 32,33,† Simina M. Boca , S. Joshua Swamidass , Austin Huang , Anthony Gitter , 2,† Casey S. Greene ☯ — Author order was determined with a randomized algorithm † — To whom correspondence should be addressed:
[email protected] (A.G.) and
[email protected] (C.S.G.) 1. Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI 2.