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Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, et al. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (NIPS 2017). https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html

Ma W, Qiu Z, Song J, Li J, Cheng Q, et al. 2018. A deep convolutional neural network approach for predicting phenotypes from genotypes. Planta 248(5):1307−18

Wang K, Ali Abid M, Rasheed A, Crossa J, Hearne S, et al. 2023. DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants. Molecular Plant 16:279−93

Gao P, Zhao H, Luo Z, Lin Y, Feng W, et al. 2023. SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding. Briefings in Bioinformatics 24(6):bbad349

Jubair S, Tremblay-Savard O, Domaratzki M. 2023. G×ENet: Novel fully connected neural network based approaches to incorporate G×E for predicting wheat yield. Artificial Intelligence in Agriculture 8:60−76

Liu B, Yang F, Huang DS, Chou KC. 2018. iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics 34:33−40

Yao L, Xie P, Guan J, Chung CR, Huang Y, et al. 2024. CapsEnhancer: an effective computational framework for identifying enhancers based on chaos game representation and capsule network. Journal of Chemical Information and Modeling 64:5725−36

Panta M, Mishra A, Hoque MT, Atallah J. 2021. ClassifyTE: a stacking-based prediction of hierarchical classification of transposable elements. Bioinformatics 37(17):2529−36

Yan H, Bombarely A, Li S. 2020. DeepTE: a computational method for de novo classification of transposons with convolutional neural network. Bioinformatics 36(15):4269−75

Pan JH, Du PF. 2023. SilenceREIN: seeking silencers on anchors of chromatin loops by deep graph neural networks. Briefings in Bioinformatics 25:bbad494

Feng CQ, Zhang ZY, Zhu XJ, Lin Y, Chen W, et al. 2019. iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators. Bioinformatics 35(9):1469−77

Yu Z, Su Y, Lu Y, Yang Y, Wang F, et al. 2023. Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA. Nature Communications 14:400

Zhang H, Wang Y, Pan Z, Sun X, Mou M, et al. 2022. ncRNAInter: a novel strategy based on graph neural network to discover interactions between lncRNA and miRNA. Briefings in Bioinformatics 23(6):bbac411

Zhang J, Hu X, Jiang Z, Song B, Quan W. 2019. Predicting disease-related RNA associations based on graph convolutional attention network. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 18–21 November 2019, San Diego, CA, USA. pp 177–82. doi: 10.1109/BIBM47256.2019.8983191

Han P, Yang P, Zhao P, Shang S, Liu Y, et al. 2019. GCN-MF: disease-gene association identification by graph convolutional networks and matrix factorization. Proceedings of the 25 ^th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 4–8 August 2019, Anchorage, AK, USA. New York, USA: ACM. pp. 705–13. doi: 10.1145/3292500.3330912

Ji Y, Zhou Z, Liu H, Davuluri RV. 2021. DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome. Bioinformatics 37(15):2112−20

Zhou Z, Ji Y, Li W, Dutta P, Davuluri R, et al. 2024. DNABERT-2: efficient foundation model and benchmark for multi-species genome. arXiv Preprint

Dalla-Torre H, Gonzalez L, Mendoza-Revilla J, Lopez Carranza N, Grzywaczewski AH, et al. 2025. Nucleotide Transformer: building and evaluating robust foundation models for human genomics. Nature Methods 22:287−97

Mendoza-Revilla J, Trop E, Gonzalez L, Roller M, Dalla-Torre H, et al. 2024. A foundational large language model for edible plant genomes. Communications Biology 7:835

Zvyagin M, Brace A, Hippe K, Deng Y, Zhang B, et al. 2023. GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics. The International Journal of High Performance Computing Applications 37(6):683−705

Ligeti B, Szepesi-Nagy I, Bodnár B, Ligeti-Nagy N, Juhász J. 2024. ProkBERT family: genomic language models for microbiome applications. Frontiers in Microbiology 14:1331233

Sanabria M, Hirsch J, Poetsch A. 2023. The human genome' s vocabulary as proposed by the DNA language model GROVER. bioRxiv Preprint

Zhang D, Zhang W, Zhao Y, Zhang J, He B, et al. 2023. DNAGPT: a generalized pre-trained tool for multiple DNA sequence analysis tasks. bioRxiv Preprint

Nguyen E, Poli M, Faizi M, Thomas A, Birch-Sykes C, et al. 2023. HyenaDNA: long-range genomic sequence modeling at single nucleotide resolution. bioRxiv Prrprint

Zhai J, Gokaslan A, Schiff Y, Berthel A, Liu ZY, et al. 2024. Cross-species modeling of plant genomes at single nucleotide resolution using a pre-trained DNA language model. bioRxiv Preprint

Liu G, Chen L, Wu Y, Han Y, Bao Y, et al. 2025. PDLLMs: a group of tailored DNA large language models for analyzing plant genomes. Molecular Plant 18:175−78

Nguyen E, Poli M, Durrant MG, Kang B, Katrekar D, et al. 2024. Sequence modeling and design from molecular to genome scale with Evo. Science 386:eado9336

Levy, B, Xu Z, Zhao L, Kremling K, Altman R, et al. 2022. FloraBERT: Cross-species transfer learning withattention-based neural networks for geneexpression prediction. Research Square Preprint

Shao B, Yan J. 2024. A long-context language model for deciphering and generating bacteriophage genomes. Nature Communications 15:9392

Avsec Ž, Agarwal V, Visentin D, Ledsam JR, Grabska-Barwinska A, et al. 2021. Effective gene expression prediction from sequence by integrating long-range interactions. Nature Methods 18(10):1196−203

Linder J, Srivastava D, Yuan H, Agarwal V, Kelley DR. 2025. Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation. Nature Genetics

Vaishnav ED, de Boer CG, Molinet J, Yassour M, Fan L, et al. 2022. The evolution, evolvability and engineering of gene regulatory DNA. Nature 603:455−63

Kwak IY, Kim BC, Lee J, Kang T, Garry DJ, et al. 2024. Proformer: a hybrid macaron transformer model predicts expression values from promoter sequences. BMC Bioinformatics 25:81

Ding K, Dixit G, Parker BJ, Wen J. 2023. CRMnet: a deep learning model for predicting gene expression from large regulatory sequence datasets. Frontiers in Big Data 6:1113402

Le NQK, Ho QT, Nguyen TTD, Ou YY. 2021. A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information. Briefings in Bioinformatics 22:bbab005

Luo H, Chen C, Shan W, Ding P, Luo L. 2022. iEnhancer-BERT: a novel transfer learning architecture based on DNA-language model for identifying enhancers and their strength. In Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, eds. Huang DS, Jo KH, Jing J, Premaratne P, Bevilacqua V, et al. Cham: Springer. pp. 153−65. doi: 10.1007/978-3-031-13829-4_13

Li J, Wu Z, Lin W, Luo J, Zhang J, et al. 2023. iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language models. Bioinformatics Advances 3:vbad043

Huang G, Luo W, Zhang G, Zheng P, Yao Y, et al. 2022. Enhancer-LSTMAtt: a Bi-LSTM and attention-based deep learning method for enhancer recognition. Biomolecules 12(7):995

Wang Y. 2024. EnhancerBD identifing sequence feature. bioRxiv Preprint

Jia J, Lei R, Qin L, Wu G, Wei X. 2023. iEnhancer-DCSV: Predicting enhancers and their strength based on DenseNet and improved convolutional block attention module. Frontiers in Genetics 14:1132018

Mehmood F, Arshad S, Shoaib M. 2024. ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction. Briefings in Bioinformatics 25:bbae030

Bickmann L, Rodriguez M, Jiang X, Makalowski W. 2023. TEclass2: Classification of transposable elements using Transformers. BioRxiv Preprint

Abrusán G, Grundmann N, DeMester L, Makalowski W. 2009. TEclass—a tool for automated classification of unknown eukaryotic transposable elements. Bioinformatics 25(10):1329−30

Qi Y, Chen Y, Wu Y, Li Y, Gao M, et al. 2024. Comprehensive hierarchical classification of transposable elements based on deep learning. bioRxiv

Zhang H, Li J, Hu F, Lin H, Ma J. 2024. AMter: an end-to-end model for transcriptional terminators prediction by extracting semantic feature automatically based on attention mechanism. Concurrency and Computation: Practice and Experience 36(13):e8056

Shen T, Hu Z, Peng Z, Chen J, Xiong P, et al. 2022. E2Efold-3D: End-to-End Deep Learning Method for accurate de novo RNA 3D Structure Prediction. arXiv Preprint

Pearce R, Omenn GS, Zhang Y. 2022. De novo RNA tertiary structure prediction at atomic resolution using geometric potentials from deep learning. bioRxiv Preprint

Baek M, McHugh R, Anishchenko I, Jiang H, Baker D, et al. 2024. Accurate prediction of protein-nucleic acid complexes using RoseTTAFoldNA. Nature Methods 21:117−21

Wang W, Feng C, Han R, Wang Z, Ye L, et al. 2023. trRosettaRNA: automated prediction of RNA 3D structure with transformer network. Nature Communications 14:7266

Kagaya Y, Zhang Z, Ibtehaz N, Wang X, Nakamura T, et al. 2023. NuFold: a novel tertiary RNA structure prediction method using deep learning with flexible nucleobase center representation. bioRxiv Preprint

Li Y, Zhang C, Feng C, Pearce R, Lydia Freddolino P, et al. 2023. Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction. Nature Communications 14:5745

Shen T, Hu Z, Sun S, Liu D, Wong F, et al. 2024. Accurate RNA 3D structure prediction using a language model-based deep learning approach. Nature Methods 21:2287−98

Zhang P, Wu H. 2023. IChrom-deep: An attention-based deep learning model for identifying chromatin interactions. IEEE Journal of Biomedical and Health Informatics 27(9):4559−68

Yang Y, Hou Z, Wang Y, Ma H, Sun P, et al. 2022. HCRNet: high-throughput circRNA-binding event identification from CLIP-seq data using deep temporal convolutional network. Briefings in Bioinformatics 23(2):bbac027

Wang W, Chen H. 2023. Predicting miRNA-disease associations based on lncRNA-miRNA interactions and graph convolution networks. Briefings in Bioinformatics 24:bbac495

Ferdaus MM, Abdelguerfi M, Ioup E, Niles KN, Pathak K, et al. 2024. Towards trustworthy AI: a review of ethical and robust large language models. arXiv Preprint