Komar, A. A. & Hatzoglou, M. Exploring inner ribosome entry websites as therapeutic targets. Entrance. Oncol. 5, 233 (2015).
Google Scholar
Martinand-Mari, C., Lebleu, B. & Robbins, I. Oligonucleotide-based methods to inhibit human hepatitis C virus. Oligonucleotides 13, 539–548 (2003).
Google Scholar
Nulf, C. J. & Corey, D. Intracellular inhibition of hepatitis C virus (HCV) inner ribosomal entry web site (IRES)-dependent translation by peptide nucleic acids (PNAs) and locked nucleic acids (LNAs). Nucleic Acids Res. 32, 3792–3798 (2004).
Google Scholar
Filbin, M. E. & Kieft, J. S. Towards a structural understanding of IRES RNA operate. Curr. Opin. Struct. Biol. 19, 267–276 (2009).
Google Scholar
Lozano, G., Fernandez, N. & Martinez-Salas, E. Modeling three-dimensional structural motifs of viral IRES. J. Mol. Biol. 428, 767–776 (2016).
Google Scholar
Plank, T.-D. M. & Kieft, J. S. The buildings of nonprotein-coding RNAs that drive inner ribosome entry web site operate. Wiley Interdiscip. Rev. RNA 3, 195–212 (2012).
Google Scholar
Mailliot, J. & Martin, F. Viral inner ribosomal entry websites: 4 courses for one purpose. Wiley Interdiscip. Rev. RNA 9, e1458 (2018).
Google Scholar
Chen, R. et al. Engineering round RNA for enhanced protein manufacturing. Nat. Biotechnol. 41, 262–272 (2023).
Google Scholar
Choi, S.-W. & Nam, J.-W. Optimum design of artificial round RNAs. Exp. Mol. Med. 56, 1281–1292 (2024).
Google Scholar
Kolekar, P., Pataskar, A., Kulkarni-Kale, U., Pal, J. & Kulkarni, A. Irespred: net server for prediction of mobile and viral inner ribosome entry web site (IRES). Sci. Rep. 6, 27436 (2016).
Google Scholar
Zhao, J. et al. IRESfinder: figuring out RNA inner ribosome entry web site in eukaryotic cell utilizing framed k-mer options. J. Genet. Genomics 45, 403–406 (2018).
Google Scholar
Wang, J. & Gribskov, M. IRESpy: an XGBoost mannequin for prediction of inner ribosome entry websites. BMC Bioinf. 20, 409 (2019).
Google Scholar
Zhou, Y. et al. DeepCIP: a multimodal deep studying methodology for the prediction of inner ribosome entry websites of circRNAs. Comput. Biol. Med. 164, 107288 (2023).
Google Scholar
Chu, Y. et al. A 5′ UTR language mannequin for decoding untranslated areas of mRNA and performance predictions. Zenodo (2024).
Shen, T. et al. Correct RNA 3D construction prediction utilizing a language model-based deep studying strategy. Nat. Strategies 21, 2287–2298 (2024).
Google Scholar
Weingarten-Gabbay, S. et al. Comparative genetics. Systematic discovery of cap-independent translation sequences in human and viral genomes. Science 351, aad4939 (2016).
Google Scholar
Zhao, J. et al. Iresbase: a complete database of experimentally validated inner ribosome entry websites. Genom. Proteom. Bioinform. 18, 129–139 (2020).
Google Scholar
Mokrejs, M. et al. IRESite–a software for the examination of viral and mobile inner ribosome entry websites. Nucleic Acids Res. 38, D131–D136 (2010).
Google Scholar
Kalvari, I. et al. Rfam 14: expanded protection of metagenomic, viral and microRNA households. Nucleic Acids Res. 49, D192–D200 (2021).
Google Scholar
Ho, J., Jain, A. & Abbeel, P. Denoising diffusion probabilistic fashions. Preprint at (2020).
Zhao, Y., Oono, Okay., Takizawa, H. & Kotera, M. GenerRNA: a generative pre-trained language mannequin for de novo RNA design. PLoS ONE 19, e0310814 (2024).
Google Scholar
Thoma, C., Bergamini, G., Galy, B., Hundsdoerfer, P. & Hentze, M. W. Enhancement of IRES-mediated translation of the c-myc and BiP mRNAs by the poly(A) tail is unbiased of intact eIF4G and PABP. Mol. Cell 15, 925–935 (2004).
Google Scholar
Li, H. et al. riboCIRC: a complete database of translatable circRNAs. Genome Biol. 22, 79 (2021).
Google Scholar
Gritsenko, A. A. et al. Sequence options of viral AND human inner ribosome entry websites predictive of their exercise. PLoS Comput. Biol. 13, e1005734 (2017).
Google Scholar
Dvir, S. et al. Deciphering the foundations by which 5′-UTR sequences have an effect on protein expression in yeast. Proc. Natl Acad. Sci. USA 110, E2792–E2801 (2013).
Google Scholar
Hie, B. L. et al. Environment friendly evolution of human antibodies from normal protein language fashions. Nat. Biotechnol. 42, 275–283 (2024).
Google Scholar
Ronneberger, O., Fischer, P. & Brox, T. U-net: convolutional networks for biomedical picture segmentation. In Medical Picture Computing and Pc-Assisted Intervention – MICCAI 2015 234–241 (Springer, 2015).
He, Okay. et al. Deep residual studying for picture recognition. In Proc. IEEE Convention on Pc Imaginative and prescient and Sample Recognition 770–778 (IEEE, 2016).
Vaswani, A. et al. Consideration is all you want. In Proc. thirty first Worldwide Convention on Neural Info Processing System 6000–6010 (NIPS, 2017).
Shen, Z., Zhang, M., Zhao, H., Yi, S. & Li, H. Environment friendly consideration: consideration with linear complexities. In Proc. IEEE/CVF Winter Convention on Purposes of Pc Imaginative and prescient 3531–3539 (IEEE, 2021).
Hughes, N. W. et al. Machine-learning-optimized Cas12a barcoding allows the restoration of single-cell lineages and transcriptional profiles. Mol. Cell 82, 3103–3118 (2022).
Google Scholar
Yin, D. et al. Focusing on herpes simplex virus with CRISPR–Cas9 cures herpetic stromal keratitis in mice. Nat. Biotechnol. 39, 567–577 (2021).
Google Scholar
Yin, D. et al. Dendritic-cell-targeting virus-like particles as potent mRNA vaccine carriers. Nat. Biomed. Eng. 9, 185–200 (2025).
Google Scholar
Chu, Y. a96123155/IRES_Prediction_Design: IRES-AI. Zenodo (2026).



