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RNA plays a pivotal role in translating genetic instructions into functional outcomes, underscoring its importance in biological processes and disease mechanisms.
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Rnam5cfinder: a web-server for predicting rna 5-methylcytosine (m5c) sites based on random forest
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F. Runge, D. Stoll, S. Falkner, and F. Hutter · 2018
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Evaluation of deep learning in non-coding RNA classification
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Interpretable rna foundation model from unannotated data for highly accurate rna structure and function predictions
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Ensembl 2022
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Flashattention: Fast and memory-efficient exact attention with io-awareness
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Robust deep learning–based protein sequence design using proteinmpnn
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Ufold: fast and accurate rna secondary structure prediction with deep learning
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A deep neural network for predicting and engineering alternative polyadenylation
N. Bogard, J. Linder, A. B. Rosenberg, and G. Seelig · 2019
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Dynamic and reversible rna n6-methyladenosine methylation
H.-C. Duan, Y. Wang, and G. Jia · 2019
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PyTorch Lightning, Mar. 2019
W. Falcon and The PyTorch Lightning team · 2019
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The ucsc genome browser database: 2019 update
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Linearfold: linear-time approximate rna folding by 5’-to-3’dynamic programming and beam search
L. Huang, H. Zhang, D. Deng, K. Zhao, K. Liu, D. A. Hendrix, and D. H. Mathews · 2019
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Generative models for graph-based protein design
J. Ingraham, V. Garg, R. Barzilay, and T. Jaakkola · 2019
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Predicting splicing from primary sequence with deep learning
K. Jaganathan, S. Kyriazopoulou Panagiotopoulou, J. F. McRae, S. F. Darbandi, D. Knowles, Y. I. Li, J. A. Kosmicki, J. Arbelaez, W. Cui, G. B. Schwartz, E. D. Chow, E. Kanterakis, H. Gao, A. Kia, S. Batzoglou, S. J. Sanders, and K. K.-H. Farh · 2019
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Z. Gao, C. Tan, P. Chacón, and S. Z. Li · 2022
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Multi-modal guided attention for live video comments generation
Y. Ren, Y. Yuan, and L. Chen · 2022
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Peer: a comprehensive and multi-task benchmark for protein sequence understanding
M. Xu, Z. Zhang, J. Lu, Z. Zhu, Y. Zhang, M. Chang, R. Liu, and J. Tang · 2022
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Impact of dataset on the study of crop disease image recognition
Y. Yuan, L. Chen, Y. Ren, S. Wang, and Y. Li · 2022
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RNA-based therapeutics: an overview and prospectus
Y. Zhu, L. Zhu, X. Wang, and H. Jin · 2022
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The master database of all possible rna sequences and its integration with rnacmap for rna homology search
K. Chen, T. Litfin, J. Singh, J. Zhan, and Y. Zhou · 2023
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Self-supervised learning on millions of pre-mrna sequences improves sequence-based rna splicing prediction
K. Chen, Y. Zhou, M. Ding, Y. Wang, Z. Ren, and Y. Yang · 2023
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The nucleotide transformer: Building and evaluating robust foundation models for human genomics
H. Dalla-Torre, L. Gonzalez, J. Mendoza-Revilla, N. L. Carranza, A. H. Grzywaczewski, F. Oteri, C. Dallago, E. Trop, B. P. de Almeida, H. Sirelkhatim, et al · 2023
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Flashattention-2: Faster attention with better parallelism and work partitioning
T. Dao · 2023
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Proteininvbench: Benchmarking protein inverse folding on diverse tasks, models, and metrics
Z. Gao, C. Tan, Y. Zhang, X. Chen, L. Wu, and S. Z. Li · 2023
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Genomic benchmarks: a collection of datasets for genomic sequence classification
K. Grešová, V. Martinek, D. Čechák, P. Šimeček, and P. Alexiou · 2023
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Rethinking the bert-like pretraining for dna sequences
C. Liang, W. Bai, L. Qiao, Y. Ren, J. Sun, P. Ye, H. Yan, X. Ma, W. Zuo, and W. Ouyang · 2023
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Bend: Benchmarking dna language models on biologically meaningful tasks
F. I. Marin, F. Teufel, M. Horrender, D. Madsen, D. Pultz, O. Winther, and W. Boomsma · 2023
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Rational design of microrna-responsive switch for programmable translational control in mammalian cells
H. Ning, G. Liu, L. Li, Q. Liu, H. Huang, and Z. Xie · 2023
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Proteingym: Large-scale benchmarks for protein fitness prediction and design
P. Notin, A. W. Kollasch, D. Ritter, L. V. Niekerk, S. Paul, H. Spinner, N. J. Rollins, A. Shaw, R. Orenbuch, R. Weitzman, J. Frazer, M. Dias, D. Franceschi, Y. Gal, and D. S. Marks · 2023
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Crossing the gap: Domain generalization for image captioning
Y. Ren, Z. Mao, S. Fang, Y. Lu, T. He, H. Du, Y. Zhang, and W. Ouyang · 2023
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Roformer: Enhanced transformer with rotary position embedding
J. Su, M. Ahmed, Y. Lu, S. Pan, W. Bo, and Y. Liu · 2023
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Transfer learning enables predictions in network biology
C. V. Theodoris, L. Xiao, A. Chopra, M. D. Chaffin, Z. R. Al Sayed, M. C. Hill, H. Mantineo, E. M. Brydon, Z. Zeng, X. S. Liu, et al · 2023
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Linguistically inspired roadmap for building biologically reliable protein language models
M. H. Vu, R. Akbar, P. A. Robert, B. Swiatczak, G. K. Sandve, V. Greiff, and D. T. T. Haug · 2023
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trrosettarna: automated prediction of rna 3d structure with transformer network
W. Wang, C. Feng, R. Han, Z. Wang, L. Ye, Z. Du, H. Wei, F. Zhang, Z. Peng, and J. Yang · 2023
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Deciphering 3’ utr mediated gene regulation using interpretable deep representation learning
Y. Yang, G. Li, K. Pang, W. Cao, X. Li, and Z. Zhang · 2023
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Multiple sequence alignment-based RNA language model and its application to structural inference
Y. Zhang, M. Lang, J. Jiang, Z. Gao, F. Xu, T. Litfin, K. Chen, J. Singh, X. Huang, G. Song, Y. Tian, J. Zhan, J. Chen, and Y. Zhou · 2023
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Dnabert-2: Efficient foundation model and benchmark for multi-species genome
Z. Zhou, Y. Ji, W. Li, P. Dutta, R. Davuluri, and H. Liu · 2023
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PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation
J. Ansel, E. Yang, H. He, N. Gimelshein, A. Jain, M. Voznesensky, B. Bao, P. Bell, D. Berard, E. Burovski, G. Chauhan, A. Chourdia, W. Constable, A. Desmaison, Z. DeVito, E. Ellison, W. Feng, J. Gong, M. Gschwind, B. Hirsh, S. Huang, K. Kalambarkar, L. Kirsch, M. Lazos, M. Lezcano, Y. Liang, J. Liang, Y. Lu, C. Luk, B. Maher, Y. Pan, C. Puhrsch, M. Reso, M. Saroufim, M. Y. Siraichi, H. Suk, M. Suo, P. Tillet, E. Wang, X. Wang, W. Wen, S. Zhang, X. Zhao, K. Zhou, R. Zou, A. Mathews, G. Chanan, P. Wu, and S. Chintala · 2024
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MARS and RNAcmap3: The Master Database of All Possible RNA Sequences Integrated with RNAcmap for RNA Homology Search
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A 5’ utr language model for decoding untranslated regions of mrna and function predictions
Y. Chu, D. Yu, Y. Li, K. Huang, Y. Shen, L. Cong, J. Zhang, and M. Wang · 2024
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Ribodiffusion: tertiary structure-based rna inverse folding with generative diffusion models
H. Huang, Z. Lin, D. He, L. Hong, and Y. Li · 2024
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grnade: Geometric deep learning for 3d rna inverse design
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BEND: Benchmarking DNA language models on biologically meaningful tasks
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A deep learning approach to programmable rna switches
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