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LinearFold: linear-time approximate RNA folding by 5'-to-3' dynamic programming and beam search
Open accessAbstract
Motivation: Predicting the secondary structure of an ribonucleic acid (RNA) sequence is useful in many applications. Existing algorithms [based on dynamic programming] suffer from a major limitation: their runtimes scale cubically with the RNA length, and this slowness limits their use in genome-wide applications. Results: We present a novel alternative O(n3)-time dynamic programming algorithm for RNA folding that is amenable to heuristics that make it run in O(n) time and O(n) space, while producing a high-quality approximation to the optimal solution. Inspired by incremental parsing for context-free grammars in computational linguistics, our alternative dynamic programming algorithm scans the sequence in a left-to-right (5′-to-3′) direction rather than in a bottom-up fashion, which allows us to employ the effective beam pruning heuristic. Our work, though inexact, is the first RNA folding algorithm to achieve linear runtime (and linear space) without imposing constraints on the output structure. Surprisingly, our approximate search results in even higher overall accuracy on a diverse database of sequences with known structures. More interestingly, it leads to significantly more accurate predictions on the longest sequence families in that database (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500+ nucleotides apart), both of which are well known to be challenging for the current models. Availability and implementation: Our source code is available at https://github.com/LinearFold/LinearFold, and our webserver is at http://linearfold.org (sequence limit: 100 000nt). Supplementary information: Supplementary data are available at Bioinformatics online.
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“Inspired by our previous work, LinearFold21, we further developed a left-to-right dynamic programming, which is equivalent to the above bottom-up version but explores the search space incrementally from left to right”
From Algorithm for optimized mRNA design improves stability and immunogenicity (2023) · cites this paper as [21]
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Self-citationOptimal computer folding of large RNA sequences using thermodynamics and auxiliary information
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Items 101–200 of 215
RAFFT: Efficient prediction of RNA folding pathways using the fast Fourier transform
Opuu, V., Merleau, N. S. C., Messow, V., & Smerlak, M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010448 · Cited by 2
Robust RNA secondary structure prediction with a mixture of deep learning and physics-based experts
Qiu, X. (2024) · Biology Methods and Protocols · doi:10.1093/biomethods/bpae097 · Cited by 3
“Here we opt for LinearFold [35] for its linear dependence of computing time on the RNA sequence length and competitive performances against other MFE-based methods such as RNAstructure [47] and RNAfold [44].”
On inputs to deep learning for RNA 3D structure predictionPreprint
Szikszai, M., Magnus, M., Kadyan, S., & Rivas, E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.14.638364 · Cited by 3
“Some structural RNA families, such as SSU and LSU rRNA subunits, contain long-range base pairs that are more than 500 residues apart, and are difficult to predict for traditional dynamic programming algorithms (Huang et al., 2019).”
Cross-talk between RNA secondary and three-dimensional structure prediction: a comprehensive studyPreprint
Wang, D., Jiang, Y., He, L., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.23.661015 · Cited by 3
Zhou, T., Malik, A., Tang, W. Y., Mathews, D. H., & Huang, L. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-90252-9_10 · Cited by 2
“ArchiveII, comprising native RNA structures, spans 10 families [16, 28, 11, 8, 27, 40] of naturally occurring RNA and is used to evaluate RNA folding [18].”
Self-citationRational design of therapeutic RNA molecules
Fang, F., & Wei, B. (2025) · Trends in Chemistry · doi:10.1016/j.trechm.2025.09.016 · Cited by 2
Zhou, T., Malik, A., TANG, W. Y., Mathews, D. H., & Huang, L. P. (2025) · Journal of Computational Biology · doi:10.1177/15578666251398551 · Cited by 2
“LinearFold also provides the functionality for constraint folding, which is used in Algorithm 3.3 to obtain MFE CC ð x i ! j Þ and uMFE CC ð x i ! j Þ .”
InfluentialSelf-citationExploring Secondary Structure Predictions for RNA-Targeted Drug Discovery: Power and Challenges
Zhang, Z., Dolcetti, G., Tyrchan, C., Maria, L. D., Bussi, G., & Czechtizky, W. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00108 · Cited by 2
Digital Technology und Artificial Intelligence Facing COVID-19
Rhalem, W., Raji, M., Aqili, N., et al. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-90639-9_102 · Cited by 2
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Omnes, L., Angel, É., Bartet, P., et al. (2023) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe60311.2023.00011 · Cited by 2
“We performed a benchmark of MXfold2 [21], Linearfold [7] and our proposed approach on the bpRNA 1m database, Fig.”
BackgroundMethodResultExploring functional conservation in silico: a new machine learning approach to RNA-editing
Zawisza-Álvarez, M., Peñuela-Melero, J., Vegas, E., Reverter, F., García‐Fernàndez, J., & Herrera-Úbeda, C. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae332 · Cited by 2
Marcius, D., Ropii, B., Safitri, D. A., Ulum, M. F., Nugrahapraja, H., & Anshori, I. (2024) · Molecular Systems Design & Engineering · doi:10.1039/d4me00073k · Cited by 2
DeepSloop: A Recurrent Neural Network Learns Complex Rules to Detect Stem-Loop-Forming RNA Sequences
Deng, D., Douglas, H., & Hendrix, D. A. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983346 · Cited by 2
Self-citationThe architecture of the SARS-CoV-2 RNA genomePreprint
Cao, C., Cai, Z., Xiao, X., Rao, J. Y., Hu, N., Yang, M., Xing, X., Zhou, B., Wang, X., Wang, J., et al. (2020) · Research Square · doi:10.21203/rs.3.rs-132578/v1 · Cited by 2
Qiu, X. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.29.498185 · Cited by 2
“LinearFold (34), SimFold (35), CONTRAfold (36), ContextFold(37), and Centroidfold (38).”
BackgroundsincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure
Bugnon, L. A., Di Persia, L., Gerard, M., Raad, J., Prochetto, S., Fenoy, E., Chorostecki, U., Ariel, F., Stegmayer, G., & Milone, D. H. (2023) · doi:10.1101/2023.10.10.561771 · Cited by 2
A Unified Framework for Model-Informed and Agentic RNA DesignPreprint
Riley, A. T., Vlasity, M., Becicka, W. M., Huang, J., Pang, S., Wong, W. W., Grinstaff, M. W., & Green, A. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.17.659751 · Cited by 3
“MFE values for CDS elements were calculated using LinearFold 46 .”
Analysis of lncRNAs in Lupinus mutabilis (Tarwi) and Their Potential Role in Drought Response
Hidalgo, M., Ramos, C., & Zolla, G. (2023) · Non-Coding RNA · doi:10.3390/ncrna9050048 · Cited by 1
Neugroschl, A., & Catrina, I. E. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011418 · Cited by 1
“However, computational time significantly increases when applying minimization algorithms to folding long RNA targets (>11,000-nt), and a dynamic programming algorithm has been shown to not only produce the MFE structure much faster, but also with improved accuracy for long RNA targets [46].”
BackgroundRNA secondary structure prediction by conducting multi-class classifications
Yang, J., Sato, K., Loza, M., Park, S., & Nakai, K. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.04.001 · Cited by 1
Structural prediction of potent non-coding RNAs
Beura, A., Manjunath, G. K., Dakal, T. C., & Kumar, A. (2025) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2025.05.002 · Cited by 1
Ju, Y.-R., Cho, L.-S., & Lu, C. L. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3596771 · Cited by 1
“Restrictions apply. et al. [6] further utilized a popular pruning technique, called beam search [12], to significantly reduce the search space of their algorithm without sacrificing much of its accuracy.”
Takahashi, S., Hamada, M., Tateishi‐Karimata, H., & Sugimoto, N. (2025) · RSC Chemical Biology · doi:10.1039/d5cb00105f · Cited by 1
Wang, X. R., Zhang, Y., Yang, L., & Wang, R. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112648 · Cited by 1
Vahab, N., & Tyagi, S. (2025) · BMC Artificial Intelligence · doi:10.1186/s44398-025-00012-7 · Cited by 2
“For example, even on low-redundancy datasets, which are typically more challenging due to limited sequence similarity, FMs [26] achieve higher accuracy scores compared to earlier DL approaches [20, 90].”
Simon, A. E., Needham, J. M., Mikkelsen, A. A., et al. (2026) · Journal of Virology · doi:10.1128/jvi.02209-25 · Cited by 1
“Full-length viral RNA structures were predicted using Linearfold (55), and local RNA regions were also folded using RNAStructure (https://rna.urmc.rochester.edu/RNAstruc tureWeb/Servers/Predict1/Predict1.html).”
Omnes, L., Angel, É., & Tahi, F. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169865 · Cited by 1
Probabilistic RNA designability via interpretable ensemble approximation and dynamic decomposition
Zhou, T., Mathews, D. H., & Huang, L. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag246 · Cited by 1
“For more details, see LinearFold (Huang et al. , 2019) and LinearPartition (Zhang et al. , 2020).”
Self-citationHuang, D., Lu, X., Zhou, J., Lu, N., Li, F., Hong, B., Zhang, C., Xu, T., & Chen, E. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770854.3783943 · Cited by 1
“LinearFold shows beam pruning boosts efficiency without loss of accuracy [18], while Ask-before-Plan em-ploysmulti-agentcollaborationforuncertainty-awareplanning[43].”
Functional atlas of vertebrate viral RNA elements that stabilize RNA and enhance translation
Seo, J. J., Lee, C., 임동빈, Jeong, M., Jung, S. J., & Kim, V. N. (2026) · Cell · doi:10.1016/j.cell.2026.07.038 · Cited by 1
Length-Based Substructure Mutation Policies for Improved RNA Design in Simulated Annealing
McBride, R., & Tsang, H. H. (2022) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb55180.2022.9863040 · Cited by 1
“We consider a two canonical RNA Design data sets, RFAM [24] and the more difficult length sequences from the Eterna100 corpus [25], and five long length sequences from the ArchiveII data set [10] derived from RNA folding research.”
BackgroundMethodInfluentialThe Role of AI in Mobile Apps to Combat Future Pandemics: A COVID-19 Case Study
Alkasassbeh, J. S., Alawneh, T. A., Al-Qaisi, A., et al. (2023) · doi:10.1109/eiceeai60672.2023.10590538 · Cited by 1
“The most important aspect of Baidu ’s contribution to fighting the COVID-19 pandemic in China is the formation of the Linearfold algorithm [15].”
BackgroundSu, S., Hu, H., Liu, K., Liu, S., Luo, Z., Yu, J., Jiang, T., Li, X., Sun, C., Yu, L., et al. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319399 · Cited by 1
“RNA folding free energy was calculated using linear-Fold [23] with default parameters, using a thermodynamic-based model.”
Self-citationWhole-genome scanning reveals selection mechanisms in epipelagic Chaetoceros diatom populationsPreprint
Nef, C., Madoui, M., Pelletier, É., & Bowler, C. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.19.492674 · Cited by 1
“…nucleotide substitution modified the predicted RNA secondary structure through an analysis conducted on the LinearFold and RNAfold web servers (Gruber et al. 2008; Huang et al. 2019), as silent mutations may impact haplotypes as for instance changes in RNA secondary structure (Sauna et al. 2007).”
MethodInfluentialDu, S., Kaynar, G., Li, J., You, Z., Tang, S., & Kingsford, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.12.705465 · Cited by 1
“This approach reduces overall computational cost without sacrificing prediction accuracy (Pearson correlation above 0.9 between LinearFold and ViennaRNA [Huang et al., 2019]).”
InfluentialProgrammed ribosomal frameshifts, and how to find themPreprint
McNair, K. A., Salamon, P., Edwards, R. A., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.10.536325
Secondary structural characterization of non-coding RNAs
O’Leary, C. A., Rouse, W. B., Peterson, J. M., Tompkins, V. S., Andrews, R. J., McCown, P. J., & Moss, W. N. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90406-3.00001-4
TransUFold: Unlocking the structural complexity of short and long RNA with pseudoknots
Wang, Y., Zhang, H., Xu, Z., Zhang, S., & Guo, R. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023854 · Cited by 1
Self-citationSynJax: Structured Probability Distributions for JAX
Stanojević, M., & Sartran, L. (2023) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2023.emnlp-demo.32 · Cited by 5
Attention-Based RNA Secondary Structure Prediction
Hu, L., Yang, X., Si, Y., Chen, J., Ye, X., Wang, Z., & Wu, F. Q. (2024) · Lecture notes in computer science · doi:10.1007/978-981-99-9119-8_36
Gaucherand, L., & Gaglia, M. M. (2024) · médecine/sciences · doi:10.1051/medsci/2023204
Language Recognition through Context-Free Grammars and Natural Language Processing
de México, U. A. D. E., & Hector, C.-H. (2023) · ECORFAN eBooks · doi:10.35429/h.2023.13.25.39
Computer Technology and Innovation
de Jocotitlán, T. D. E. S., & Adriana Reyes-Nava (2023) · ECORFAN eBooks · doi:10.35429/h.2023.13.1.122
Lee, D., Shi, M., Moran, J. R., Wall, M., Zhang, J., Liu, J. J., Fitzgerald, D., Kyono, Y., Ma, L., White, K. P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13352114
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Gray, M., Will, S., & Jabbari, H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.05.543808
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Sharma, N., & Mitra, P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.26.609824
“To reduce the time complexity of dynamic programming-based methods, Linearfold [22] and LinearPartition [23] have proposed an approximate MFE structure in linear time.”
Partition, Predict and Assemble: Targeting Long RNA Secondary Structure Prediction
Fan, X., Cui, Y., Zhao, Z., He, Z., Zhao, Q., & Wang, Z. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822375
“…the proposed PPA framework, we combined our ELPBert and six state-of-the-art RNA secondary structure prediction methods (RNAfold [30], LinearFold [31], CON-TRAfold [32], mfold [33], SPOT-RNA [13], and MXfold2 [14]), i.e., partition the RNA sequence with our ELPBert, predict the secondary…”
Calderón-Arce, C., Crespo-Mariño, J. L., & Mora-Rodríguez, R. A. (2024) · IEEE Central America and Panama Convention (CONCAPAN) · doi:10.1109/concapan63470.2024.10933592
“Some of the most commonly used methods and libraries for predicting RNA secondary structures are based on statistical learning, such as CONTRAFold [8]; thermodynamics, such as RNAstructure [9]; and dynamic programming, such as RNAfold [10] and LinearFold [11].”
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Gan, L., Wang, X., Zhou, Y., & Zhu, M. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3556876
“For RNA secondary structure prediction, RNAfold [30] was used over other advanced methods such as FocusFold [31], CONTRAFold [32], and LinearFold [33], based on the recommendations by Binet et al. [34].”
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“For LinearFold, the beam size and the option to use the machine learned scoring model (LinearFold-C) can be chosen.”
Li, S., & Liu, Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5601714
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Brase, L., Creamer, D. R., Shapovalova, Y., Ashe, M., Ashe, H. L., & Rattray, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.04.692303
Binet, T., Padiolleau‐Lefèvre, S., Octave, S., Avalle, B., & Maffucci, I. (2024) · Figshare · doi:10.6084/m9.figshare.26637456
Binet, T., Padiolleau‐Lefèvre, S., Octave, S., Avalle, B., & Maffucci, I. (2024) · Figshare · doi:10.6084/m9.figshare.26637483
Binet, T., Padiolleau‐Lefèvre, S., Octave, S., Avalle, B., & Maffucci, I. (2024) · Figshare · doi:10.6084/m9.figshare.26637474
Binet, T., Padiolleau‐Lefèvre, S., Octave, S., Avalle, B., & Maffucci, I. (2024) · Figshare · doi:10.6084/m9.figshare.26637450
Binet, T., Padiolleau‐Lefèvre, S., Octave, S., Avalle, B., & Maffucci, I. (2024) · Figshare · doi:10.6084/m9.figshare.26637468
Binet, T., Padiolleau‐Lefèvre, S., Octave, S., Avalle, B., & Maffucci, I. (2024) · Figshare · doi:10.6084/m9.figshare.26637471
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Lee, D., Shi, M., Moran, J., Wall, M., Zhang, J., Liu, J. J., Fitzgerald, D., Kyono, Y., Ma, L., White, K. P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13352102
Gray, M., Chester, S., & Jabbari, H. (2022) · Figshare · doi:10.6084/m9.figshare.19703849
Gray, M., Chester, S., & Jabbari, H. (2022) · Figshare · doi:10.6084/m9.figshare.19703840
Binet, T., Padiolleau‐Lefèvre, S., Octave, S., Avalle, B., & Maffucci, I. (2024) · Figshare · doi:10.6084/m9.figshare.26637486
McNair, K., Salamon, P., Edwards, R. A., & Segall, A. M. (2024) · Figshare · doi:10.6084/m9.figshare.25271812
Binet, T., Padiolleau‐Lefèvre, S., Octave, S., Avalle, B., & Maffucci, I. (2024) · Figshare · doi:10.6084/m9.figshare.26637480
Binet, T., Padiolleau‐Lefèvre, S., Octave, S., Avalle, B., & Maffucci, I. (2024) · Figshare · doi:10.6084/m9.figshare.26637453
Lee, D., Shi, M., Moran, J., Wall, M., Zhang, J., Liu, J. J., Fitzgerald, D., Kyono, Y., Ma, L., White, K. P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13352099
Binet, T., Padiolleau‐Lefèvre, S., Octave, S., Avalle, B., & Maffucci, I. (2024) · Figshare · doi:10.6084/m9.figshare.24532290
Binet, T., Padiolleau‐Lefèvre, S., Octave, S., Avalle, B., & Maffucci, I. (2024) · Figshare · doi:10.6084/m9.figshare.26637477
Binet, T., Padiolleau‐Lefèvre, S., Octave, S., Avalle, B., & Maffucci, I. (2024) · Figshare · doi:10.6084/m9.figshare.26637459
Gray, M., Chester, S., & Jabbari, H. (2022) · Figshare · doi:10.6084/m9.figshare.19703846
Binet, T., Padiolleau‐Lefèvre, S., Octave, S., Avalle, B., & Maffucci, I. (2023) · Figshare · doi:10.6084/m9.figshare.24531948
Lee, D., Shi, M., Moran, J., Wall, M., Zhang, J., Liu, J. J., Fitzgerald, D., Kyono, Y., Ma, L., White, K. P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13352105
Gray, M., Chester, S., & Jabbari, H. (2022) · Figshare · doi:10.6084/m9.figshare.19703843
Binet, T., Padiolleau‐Lefèvre, S., Octave, S., Avalle, B., & Maffucci, I. (2024) · Figshare · doi:10.6084/m9.figshare.26637462
Binet, T., Padiolleau‐Lefèvre, S., Octave, S., Avalle, B., & Maffucci, I. (2024) · Figshare · doi:10.6084/m9.figshare.26637465
Wang, L., Zhang, H., Li, X., Wang, Y., & Xue, Z. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.18.676970
“At its foundation, our approach employs a meta-learning strategy by assimilating structural priors from a diverse ensemble of orthogonal prediction tools—including thermodynamic (LinearFold 25 ), probabilistic (CONTRAfold 15 ), and deep learning-based (UFold 20 , SPOT-RNA 18 ) predictors.”
Self-citationHierarchical analysis of RNA secondary structures with pseudoknots based on sections
Masuki, R., Liew, D., & Yong, E. H. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013904
Tu, Z., Fan, W., & Zhang, Y. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356514
“To balance prediction accuracy and computational efficiency, we elaborately selected the LinearFold algorithm [13] for secondary structure prediction.”
Genetic variation shapes human mRNA translation and disease riskPreprint
Wang, S., Chen, C., Xiao, X. G., & Wang, Z. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.10.705206
“RNA secondary structures harboring the reference and alternative alleles were predicted using LinearFold and full-length mRNA sequences 87 .”
CPLfold: Chimeric and Pseudoknot-capable almost Linear-time RNA Secondary Structure PredictionPreprint
Wang, K., Kudla, G. R., & Cohen, S. B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.12.704779
Liu, Y.-S., Ju, Y.-R., Chang, K.-W., et al. (2026) · Bioinformatics Advances · doi:10.1093/bioadv/vbag060
“To further reduce computational time, they incorporated beam search (Huang et al .”
Graph-based RNA structural representation reveals determinants of subcellular localizationPreprint
Hao, Y., Sun, H., Ran, Z., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.23.707397
Marino, S. M., Husak, V., & Tebaldi, T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.04.709501
Crosstalk between RNA secondary and three-dimensional structure prediction: a comprehensive study
Wang, D., Jiang, Y., He, L., Zhang, L., Zhou, R., & Zhang, D. (2026) · RNA Biology · doi:10.1080/15476286.2026.2655096 · Cited by 2
“So far, numerous models have been proposed to obtain the RNA 2D structure from its sequence, such as RNAfold [58], RNAStructure [59], CONTRAfold [60], Mfold [61–63], HotKnots [64], MXfold2 [65], NUPACK [66], SPOT-RNA [67], PETfold [68], and more [69–76].”
Metadata
- Authors
- Huang, L., Zhang, H., Deng, D., Zhao, K., Liu, K., Hendrix, D. A., & Mathews, D. H.
- Affiliations
- Oregon State University (US)
- Baidu (China) (CN)
- University of Rochester Medicine (US)
- Corresponding author
- Huang, Liang
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-07
- Volume
- 35
- Issue
- 14
- Pages
- i295-i304
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC6681470
- OpenAlex
- W2954102902
- Semantic Scholar
- 3aec8d65f4879bb9615fc09c3839036b60425b75
- Cited by
- 207
- References
- 63
- Citations by year
- 2026: 34 · 2025: 41 · 2024: 44 · 2023: 38 · 2022: 25 · 2021: 11 · 2020: 10 · 2019: 4
- FWCI
- 4.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (IIS-1656051, IIS-1817231)
- National Institutes of Health (R56 AG053460, R21 AG052950, R01 GM076485)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Mathematics › Theoretical Computer Science › Algorithms and Data Compression
- Keywords
- RNA secondary structure prediction, RNA folding, beam search, incremental parsing, context-free grammars, 16S rRNA, 23S rRNA
- MeSH terms
- Nucleic Acid Conformation, RNA, Software, Sequence Analysis, RNA, RNA Folding
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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