TY - GEN
T1 - RNA SECONDARY STRUCTURE PREDICTION BY LEARNING UNROLLED ALGORITHMS
AU - Chen, Xinshi
AU - Li, Yu
AU - Umarov, Ramzan
AU - Gao, Xin
AU - Song, Le
N1 - KAUST Repository Item: Exported on 2023-04-06
Acknowledged KAUST grant number(s): BAS/1/1624-01, FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, REI/1/0018-01-01, URF/1/4098-01-01
Acknowledgements: We would like to thank anonymous reviewers for providing constructive feedbacks. This work is supported in part by NSF grants CDS&E-1900017 D3SC, CCF-1836936 FMitF, IIS-1841351, CAREER IIS-1350983 to L.S. and grants from King Abdullah University of Science and Technology, under award numbers BAS/1/1624-01, FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, REI/1/0018-01-01, and URF/1/4098-01-01.
PY - 2020
Y1 - 2020
N2 - In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints in the problem. The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled algorithm for constrained programming as the template for deep architectures to enforce constraints. With comprehensive experiments on benchmark datasets, we demonstrate the superior performance of E2Efold: it predicts significantly better structures compared to previous SOTA (especially for pseudoknotted structures), while being as efficient as the fastest algorithms in terms of inference time.
AB - In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints in the problem. The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled algorithm for constrained programming as the template for deep architectures to enforce constraints. With comprehensive experiments on benchmark datasets, we demonstrate the superior performance of E2Efold: it predicts significantly better structures compared to previous SOTA (especially for pseudoknotted structures), while being as efficient as the fastest algorithms in terms of inference time.
UR - http://hdl.handle.net/10754/690847
UR - https://openreview.net/forum?id=S1eALyrYDH
UR - http://www.scopus.com/inward/record.url?scp=85150617831&partnerID=8YFLogxK
M3 - Conference contribution
BT - 8th International Conference on Learning Representations, ICLR 2020
PB - International Conference on Learning Representations, ICLR
ER -