Geometric deep learning of RNA structure

Raphael J.L. Townshend; Stephan Eismann; Andrew M. Watkins; Ramya Rangan; Masha Karelina; Rhiju Das; Ron O. Dror

doi:10.1126/science.abe5650

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

Keywords

RNAArtificial intelligenceDeep learningComputer sciencePerspective (graphical)Machine learningNucleic acid structureTraining setComputational biologyBiologyGeneGenetics

Affiliated Institutions

Stanford University US

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Publication Info

Year: 2021
Type: article
Volume: 373
Issue: 6558
Pages: 1047-1051
Citations: 417
Access: Closed

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APA Style

                            
                                
                                    Raphael J.L. Townshend, 
                                
                                    Stephan Eismann, 
                                
                                    Andrew M. Watkins
                                
                                et al.
                            
                            (2021). 
                            Geometric deep learning of RNA structure. 
                            Science
                            , 373
                            (6558)
                            , 1047-1051.
                            https://doi.org/10.1126/science.abe5650
                        

Identifiers

DOI: 10.1126/science.abe5650