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Non-coding RNA structure and function are essential to understanding various biological processes, such as cell signaling, gene expression, and post-transcriptional regulations.
Rna secondary structure: A complete mathematical analysis
Waterman, M. S. & Smith, T. F · 1978
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Rna secondary structure: A complete mathematical analysis
Waterman, M. S. & Smith, T. F · 1978
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Rna secondary structures and their prediction
Zuker, M. & Sankoff, D · 1984
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Rna secondary structures and their prediction
Zuker, M. & Sankoff, D · 1984
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Fast folding and comparison of rna secondary structures
Hofacker, I. L. et al · 1994
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Fast folding and comparison of rna secondary structures
Hofacker, I. L. et al · 1994
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Rna structure and stability
Nowakowski, J. & Tinoco Jr, I · 1997
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Rna structure and stability
Nowakowski, J. & Tinoco Jr, I · 1997
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An updated recursive algorithm for rna secondary structure prediction with improved thermodynamic parameters (1998)
Mathews, D. H., Andre, T. C., Kim, J., Turner, D. H. & Zuker, M · 1998
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An updated recursive algorithm for rna secondary structure prediction with improved thermodynamic parameters (1998)
Mathews, D. H., Andre, T. C., Kim, J., Turner, D. H. & Zuker, M · 1998
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Rna: versatility in form and function
Caprara, M. G. & Nilsen, T. W · 2000
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Rna: versatility in form and function
Caprara, M. G. & Nilsen, T. W · 2000
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The accuracy of ribosomal rna comparative structure models
Gutell, R. R., Lee, J. C. & Cannone, J. J · 2002
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Fast and accurate phylogeny reconstruction algorithms based on the minimum-evolution principle
Desper, R. & Gascuel, O · 2002
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The accuracy of ribosomal rna comparative structure models
Gutell, R. R., Lee, J. C. & Cannone, J. J · 2002
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Fast and accurate phylogeny reconstruction algorithms based on the minimum-evolution principle
Desper, R. & Gascuel, O · 2002
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Mfold web server for nucleic acid folding and hybridization prediction
Zuker, M · 2003
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Rnasoft: a suite of rna secondary structure prediction and design software tools
Andronescu, M., Aguirre-Hernandez, R., Condon, A. & Hoos, H. H · 2003
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Mfold web server for nucleic acid folding and hybridization prediction
Zuker, M · 2003
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Rnasoft: a suite of rna secondary structure prediction and design software tools
Andronescu, M., Aguirre-Hernandez, R., Condon, A. & Hoos, H. H · 2003
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Contrafold: Rna secondary structure prediction without physics-based models
Do, C. B., Woods, D. A. & Batzoglou, S · 2006
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Contrafold: Rna secondary structure prediction without physics-based models
Do, C. B., Woods, D. A. & Batzoglou, S · 2006
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Unifying evolutionary and thermodynamic information for rna folding of multiple alignments
Seemann, S. E., Gorodkin, J. & Backofen, R · 2008
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Unifying evolutionary and thermodynamic information for rna folding of multiple alignments
Seemann, S. E., Gorodkin, J. & Backofen, R · 2008
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Centroidfold: a web server for rna secondary structure prediction
Sato, K., Hamada, M., Asai, K. & Mituyama, T · 2009
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Centroidfold: a web server for rna secondary structure prediction
Sato, K., Hamada, M., Asai, K. & Mituyama, T · 2009
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Rnastructure: software for rna secondary structure prediction and analysis
Reuter, J. S. & Mathews, D. H · 2010
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Rnastructure: software for rna secondary structure prediction and analysis
Reuter, J. S. & Mathews, D. H · 2010
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Rna worlds: from life’s origins to diversity in gene regulation (2011)
Atkins, J. F., Gesteland, R. F. & Cech, T · 2011
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Molecular mechanisms of long noncoding rnas
Wang, K. C. & Chang, H. Y · 2011
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Viennarna package 2.0
Stadler, P. et al · 2011
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Viennarna package 2.0
Lorenz, R. et al · 2011
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Rich parameterization improves rna structure prediction
Zakov, S., Goldberg, Y., Elhadad, M. & Ziv-Ukelson, M · 2011
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Rna worlds: from life’s origins to diversity in gene regulation (2011)
Atkins, J. F., Gesteland, R. F. & Cech, T · 2011
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Molecular mechanisms of long noncoding rnas
Wang, K. C. & Chang, H. Y · 2011
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Viennarna package 2.0
Stadler, P. et al · 2011
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Viennarna package 2.0
Lorenz, R. et al · 2011
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Rich parameterization improves rna structure prediction
Zakov, S., Goldberg, Y., Elhadad, M. & Ziv-Ukelson, M · 2011
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Rna interference therapeutics for cancer: challenges and opportunities
Bora, R. S., Gupta, D., Mukkur, T. K. S. & Saini, K. S · 2012
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Cd-hit: accelerated for clustering the next-generation sequencing data
Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W · 2012
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Rna interference therapeutics for cancer: challenges and opportunities
Bora, R. S., Gupta, D., Mukkur, T. K. S. & Saini, K. S · 2012
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Cd-hit: accelerated for clustering the next-generation sequencing data
Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W · 2012
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The four ingredients of single-sequence rna secondary structure prediction. a unifying perspective
Rivas, E · 2013
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Novel cis-acting element within the capsid-coding region enhances flavivirus viral-rna replication by regulating genome cyclization
Liu, Z.-Y. et al · 2013
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The four ingredients of single-sequence rna secondary structure prediction. a unifying perspective
Rivas, E · 2013
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Novel cis-acting element within the capsid-coding region enhances flavivirus viral-rna replication by regulating genome cyclization
Liu, Z.-Y. et al · 2013
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The noncoding rna revolution—trashing old rules to forge new ones
Cech, T. R. & Steitz, J. A · 2014
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The evolution of lncrna repertoires and expression patterns in tetrapods
Necsulea, A. et al · 2014
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Graphprot: modeling binding preferences of rna-binding proteins
Maticzka, D., Lange, S. J., Costa, F. & Backofen, R · 2014
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The noncoding rna revolution—trashing old rules to forge new ones
Cech, T. R. & Steitz, J. A · 2014
Cited alongside, same era.
The evolution of lncrna repertoires and expression patterns in tetrapods
Necsulea, A. et al · 2014
Linearfold: linear-time approximate rna folding by 5’-to-3’dynamic programming and beam search
Huang, L. et al · 2019
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Rna secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning
Singh, J., Hanson, J., Paliwal, K. & Zhou, Y · 2019
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A deep learning framework to predict binding preference of rna constituents on protein surface
Lam, J. H. et al · 2019
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Human 5’ utr design and variant effect prediction from a massively parallel translation assay
Sample, P. J. et al · 2019
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A comparison of single-cell trajectory inference methods
Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y · 2019
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Alphafold at casp13
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Cited alongside, same era.
Graphprot: modeling binding preferences of rna-binding proteins
Maticzka, D., Lange, S. J., Costa, F. & Backofen, R · 2014
Cited alongside, same era.
The rna shapes studio
Janssen, S. & Giegerich, R · 2015
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Structural imprints in vivo decode rna regulatory mechanisms
Spitale, R. C. et al · 2015
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Predicting the sequence specificities of dna-and rna-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T. & Frey, B. J · 2015
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The rna shapes studio
Janssen, S. & Giegerich, R · 2015
Cited alongside, same era.
Structural imprints in vivo decode rna regulatory mechanisms
Spitale, R. C. et al · 2015
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AlQuraishi, M · 2019
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Strategies for the crispr-based therapeutics
Li, B., Niu, Y., Ji, W. & Dong, Y · 2020
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Rna secondary structure prediction by learning unrolled algorithms
Chen, X., Li, Y., Umarov, R., Gao, X. & Song, L · 2020
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Rna secondary structure packages ranked and improved by high-throughput experiments
Wayment-Steele, H. K., Kladwang, W., Participants, E. & Das, R · 2020
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Improved protein structure prediction using predicted interresidue orientations
Yang, J. et al · 2020
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Farfar2: improved de novo rosetta prediction of complex global rna folds
Watkins, A. M., Rangan, R. & Das, R · 2020
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A new coronavirus associated with human respiratory disease in china
Wu, F. et al · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A. et al · 2020
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Strategies for the crispr-based therapeutics
Li, B., Niu, Y., Ji, W. & Dong, Y · 2020
Later among the works it cites.
Rna secondary structure prediction by learning unrolled algorithms
Chen, X., Li, Y., Umarov, R., Gao, X. & Song, L · 2020
Later among the works it cites.
Rna secondary structure packages ranked and improved by high-throughput experiments
Wayment-Steele, H. K., Kladwang, W., Participants, E. & Das, R · 2020
Later among the works it cites.
Improved protein structure prediction using predicted interresidue orientations
Yang, J. et al · 2020
Later among the works it cites.
Farfar2: improved de novo rosetta prediction of complex global rna folds
Watkins, A. M., Rangan, R. & Das, R · 2020
Later among the works it cites.
A new coronavirus associated with human respiratory disease in china
Wu, F. et al · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A. et al · 2020
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Geometric deep learning of rna structure
Townshend, R. J. et al · 2021
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Rna secondary structure prediction using deep learning with thermodynamic integration
Sato, K., Akiyama, M. & Sakakibara, Y · 2021
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Ufold: fast and accurate rna secondary structure prediction with deep learning
Fu, L. et al · 2021
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Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving rna model refinement
Xiong, P., Wu, R., Zhan, J. & Zhou, Y · 2021
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Rna inter-nucleotide 3d closeness prediction by deep residual neural networks
Sun, S., Wang, W., Peng, Z. & Yang, J · 2021
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Predicting dynamic cellular protein–rna interactions by deep learning using in vivo rna structures
Sun, L. et al · 2021
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Generalized and scalable trajectory inference in single-cell omics data with via
Stassen, S. V., Yip, G. G., Wong, K. K., Ho, J. W. & Tsia, K. K · 2021
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Review of machine learning methods for rna secondary structure prediction
Zhao, Q. et al · 2021
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The architecture of the sars-cov-2 rna genome inside virion
Cao, C. et al · 2021
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Protein-rna interaction prediction with deep learning: structure matters
Wei, J., Chen, S., Zong, L., Gao, X. & Li, Y · 2021
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Rives, A. et al · 2021
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Nucleic acids research 49
Rnacentral 2021: secondary structure integration, improved sequence search and new member databases · 2021
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Geometric deep learning of rna structure
Townshend, R. J. et al · 2021
Later among the works it cites.
Rna secondary structure prediction using deep learning with thermodynamic integration
Sato, K., Akiyama, M. & Sakakibara, Y · 2021
Later among the works it cites.
Ufold: fast and accurate rna secondary structure prediction with deep learning
Fu, L. et al · 2021
Later among the works it cites.
Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving rna model refinement
Xiong, P., Wu, R., Zhan, J. & Zhou, Y · 2021
Later among the works it cites.
Rna inter-nucleotide 3d closeness prediction by deep residual neural networks
Sun, S., Wang, W., Peng, Z. & Yang, J · 2021
Later among the works it cites.
Predicting dynamic cellular protein–rna interactions by deep learning using in vivo rna structures
Sun, L. et al · 2021
Later among the works it cites.
Generalized and scalable trajectory inference in single-cell omics data with via
Stassen, S. V., Yip, G. G., Wong, K. K., Ho, J. W. & Tsia, K. K · 2021
Later among the works it cites.
Review of machine learning methods for rna secondary structure prediction
Zhao, Q. et al · 2021
Later among the works it cites.
The architecture of the sars-cov-2 rna genome inside virion
Cao, C. et al · 2021
Later among the works it cites.
Protein-rna interaction prediction with deep learning: structure matters
Wei, J., Chen, S., Zong, L., Gao, X. & Li, Y · 2021
Later among the works it cites.
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Rives, A. et al · 2021
Later among the works it cites.
Nucleic acids research 49
Rnacentral 2021: secondary structure integration, improved sequence search and new member databases · 2021
Later among the works it cites.