mlsanityData to 7 Oct 2026

RNA Secondary Structure Prediction By Learning Unrolled Algorithms

arXiv 2002.05810 ↗First posted 13 Feb 2020cs.LG stat.MLcited by 24

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Abstract (arXiv)

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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Feb 2020.

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Citing papers per year: 2021: 7; 2022: 6; 2023: 4; 2024: 7.

24 papers.

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  1. 2021Learning to Optimize: A Primer and A Benchmarkcited by 206
  2. 2021Learning Long-Term Reward Redistribution via Randomized Return Decompositioncited by 33
  3. 2022Symbolic Learning to Optimize: Towards Interpretability and Scalabilitycited by 16
  4. 2022Accurate RNA 3D structure prediction using a language model-based deep learning approachcited by 16
  5. 2024Beyond Sequence: Impact of Geometric Context for RNA Property Predictioncited by 8
  6. 2022Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Designcited by 7
  7. 2024BEACON: Benchmark for Comprehensive RNA Tasks and Language Modelscited by 7
  8. 2021Protein-RNA interaction prediction with deep learning: Structure matterscited by 6
  9. 2022uGLAD: Sparse graph recovery by optimizing deep unrolled networkscited by 6
  10. 2023RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Designcited by 6
  11. 2023Two-Stage Learning For the Flexible Job Shop Scheduling Problemcited by 4
  12. 2021ModelPred: A Framework for Predicting Trained Model from Training Datacited by 2
12 more
  1. 2023Scalable Deep Learning for RNA Secondary Structure Predictioncited by 2
  2. 2024Compact Optimality Verification for Optimization Proxiescited by 2
  3. 2022Probability estimation and structured output prediction for learning preferences in last mile deliverycited by 1
  4. 2022Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspectivecited by 1
  5. 2024RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matchingcited by 1
  6. 2021Neural representation and generation for RNA secondary structurescited by 0
  7. 2021Learned Interpretable Residual Extragradient ISTA for Sparse Codingcited by 0
  8. 2021CLMB: deep contrastive learning for robust metagenomic binningcited by 0
  9. 2023Knowledge Propagation over Conditional Independence Graphscited by 0
  10. 2024Kirigami: large convolutional kernels improve deep learning-based RNA secondary structure predictioncited by 0
  11. 2024Predicting Distance matrix with large language modelscited by 0
  12. 2024OmniGenBench: Automating Large-scale in-silico Benchmarking for Genomic Foundation Modelscited by 0