Geometric deep learning of RNA structure-Reference-Cited by-同舟云学术

Geometric deep learning of RNA structure

Published:2021-08-27 Issue:6558 Volume:373 Page:1047-1051
ISSN:0036-8075
Container-title:Science
language:en
Short-container-title:Science

Author:

Townshend Raphael J. L.¹^ORCID,Eismann Stephan¹²,Watkins Andrew M.³^ORCID,Rangan Ramya³⁴^ORCID,Karelina Maria¹⁴^ORCID,Das Rhiju³⁵^ORCID,Dror Ron O.¹⁶⁷⁸^ORCID

Affiliation:

1. Department of Computer Science, Stanford University, Stanford, CA, USA.

2. Department of Applied Physics, Stanford University, Stanford, CA, USA.

3. Department of Biochemistry, Stanford University, Stanford, CA, USA.

4. Biophysics Program, Stanford University, Stanford, CA, USA.

5. Department of Physics, Stanford University, Stanford, CA, USA.

6. Department of Structural Biology, Stanford University, Stanford, CA, USA.

7. Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA.

8. Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.

Abstract

Machine learning solves RNA puzzles RNA molecules fold into complex three-dimensional shapes that are difficult to determine experimentally or predict computationally. Understanding these structures may aid in the discovery of drugs for currently untreatable diseases. Townshend et al . introduced a machine-learning method that significantly improves prediction of RNA structures (see the Perspective by Weeks). Most other recent advances in deep learning have required a tremendous amount of data for training. The fact that this method succeeds given very little training data suggests that related methods could address unsolved problems in many fields where data are scarce. —DJ

Funder

National Institutes of Health

U.S. Department of Energy

Intel Corporation

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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