Fetching the paper…
Reading the bibliography…
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.
Central dogma of molecular biology
Francis Crick · 1970
Earlier work this paper cites.
Neural networks, adaptive optimization, and RNA secondary structure prediction
Evan W Steeg · 1993
Earlier work this paper cites.
RNA pseudoknot prediction in energy-based models
Rune B Lyngsø and Christian NS Pedersen · 2000
Earlier work this paper cites.
Novel features in the tRNA-like world of plant viral RNAs
P Fechter, J Rudinger-Thirion, C Florentz, and R Giege · 2001
Earlier work this paper cites.
Non-projective dependency parsing using spanning tree algorithms
Ryan McDonald, Fernando Pereira, Kiril Ribarov, and Jan Hajič · 2005
Earlier work this paper cites.
Pseudoknots: RNA structures with diverse functions
David W Staple and Samuel E Butcher · 2005
Earlier work this paper cites.
Contrafold: RNA secondary structure prediction without physics-based models
Chuong B Do, Daniel A Woods, and Serafim Batzoglou · 2006
Earlier work this paper cites.
Predicting RNA secondary structure by free energy minimization
David H Mathews · 2006
Earlier work this paper cites.
Prediction of RNA secondary structure by free energy minimization
David H Mathews and Douglas H Turner · 2006
Earlier work this paper cites.
Utilizing RNA interference to enhance cancer drug discovery
Elizabeth Iorns, Christopher J Lord, Nicholas Turner, and Alan Ashworth · 2007
Earlier work this paper cites.
Unafold: software for nucleic acid folding and hybridization in: Keith jm, editor.(ed.) bioinformatics methods in molecular biology, vol. 453, 2008
NR Markham and M Zuker · 2008
Earlier work this paper cites.
Improved free energy parameters for RNA pseudoknotted secondary structure prediction
Mirela S Andronescu, Cristina Pop, and Anne E Condon · 2010
Earlier work this paper cites.
Probknot: fast prediction of RNA secondary structure including pseudoknots
Stanislav Bellaousov and David H Mathews · 2010
Cited alongside, same era.
ViennaRNA package 2.0
Ronny Lorenz, Stephan H Bernhart, Christian Höner Zu Siederdissen, Hakim Tafer, Christoph Flamm, Peter F Stadler, and Ivo L Hofacker · 2011
Cited alongside, same era.
Rich parameterization improves RNA structure prediction
Shay Zakov, Yoav Goldberg, Michael Elhadad, and Michal Ziv-Ukelson · 2011
Cited alongside, same era.
RNAstructure: web servers for RNA secondary structure prediction and analysis
Stanislav Bellaousov, Jessica S Reuter, Matthew G Seetin, and David H Mathews · 2013
Cited alongside, same era.
Accurate shape-directed RNA secondary structure modeling, including pseudoknots
Christine E Hajdin, Stanislav Bellaousov, Wayne Huggins, Christopher W Leonard, David H Mathews, and Kevin M Weeks · 2013
Cited alongside, same era.
Deep unfolding: Model-based inspiration of novel deep architectures
Deepre: sequence-based enzyme ec number prediction by deep learning
Yu Li, Sheng Wang, Ramzan Umarov, Bingqing Xie, Ming Fan, Lihua Li, and Xin Gao · 2017
Later among the works it cites.
Turbofold ii: RNA structural alignment and secondary structure prediction informed by multiple homologs
Zhen Tan, Yinghan Fu, Gaurav Sharma, and David H Mathews · 2017
Later among the works it cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Later among the works it cites.
Accurate de novo prediction of protein contact map by ultra-deep learning model
Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang, and Jinbo Xu · 2017
Later among the works it cites.
Theoretical linear convergence of unfolded ista and its practical weights and thresholds
Xiaohan Chen, Jialin Liu, Zhangyang Wang, and Wotao Yin · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
John R Hershey, Jonathan Le Roux, and Felix Weninger · 2014
Cited alongside, same era.
Deep biaffine attention for neural dependency parsing
Timothy Dozat and Christopher D Manning · 2016
Cited alongside, same era.
Simple and accurate dependency parsing using bidirectional lstm feature representations
Eliyahu Kiperwasser and Yoav Goldberg · 2016
Cited alongside, same era.
Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures
Michael F Sloma and David H Mathews · 2016
Cited alongside, same era.
Auc-maximized deep convolutional neural fields for protein sequence labeling
Sheng Wang, Siqi Sun, and Jinbo Xu · 2016
Cited alongside, same era.
Optnet: Differentiable optimization as a layer in neural networks
Brandon Amos and J Zico Kolter · 2017
Cited alongside, same era.
End-to-end learning for structured prediction energy networks
David Belanger, Bishan Yang, and Andrew McCallum · 2017
Cited alongside, same era.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Later among the works it cites.
Learning protein structure with a differentiable simulator
John Ingraham, Adam Riesselman, Chris Sander, and Debora Marks · 2018
Later among the works it cites.
A smoother way to train structured prediction models
Venkata Krishna Pillutla, Vincent Roulet, Sham M Kakade, and Zaid Harchaoui · 2018
Later among the works it cites.
Linearfold: linear-time approximate RNA folding by 5’-to-3’dynamic programming and beam search
Liang Huang, He Zhang, Dezhong Deng, Kai Zhao, Kaibo Liu, David A Hendrix, and David H Mathews · 2019
Later among the works it cites.
How to benchmark RNA secondary structure prediction accuracy
David H Mathews · 2019
Later among the works it cites.
Glad: Learning sparse graph recovery
Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinvas Aluru, and Le Song · 2019
Later among the works it cites.
Hao Zhang, Chunhe Zhang, Zhi Li, Cong Li, Xu Wei, Borui Zhang, and Yuanning Liu · 2019
Later among the works it cites.