16021941 4WTWCSN6 1 apa 50 date desc year 1 249 https://airfoundry.upenn.edu/wp-content/plugins/zotpress/
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ying%20G4mer%20to%205%5Cu2019%20untranslated%20region%20%28UTR%29%20variations%2C%20we%20identify%20variants%20in%20breast%20cancer-associated%20genes%20that%20alter%20rG4%20formation%20and%20validate%20their%20impact%20on%20structure%20and%20gene%20expression.%20These%20results%20demonstrate%20the%20potential%20of%20integrating%20computational%20models%20with%20experimental%20approaches%20to%20study%20rG4%20function%2C%20especially%20in%20diseases%20where%20non-coding%20variants%20are%20often%20overlooked.%20To%20support%20broader%20applications%2C%20G4mer%20is%20available%20as%20both%20a%20web%20tool%20and%20a%20downloadable%20model.%22%2C%22genre%22%3A%22%22%2C%22repository%22%3A%22bioRxiv%22%2C%22archiveID%22%3A%22%22%2C%22date%22%3A%222024-10-03%22%2C%22DOI%22%3A%2210.1101%5C%2F2024.10.01.616124%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.biorxiv.org%5C%2Fcontent%5C%2F10.1101%5C%2F2024.10.01.616124v1%22%2C%22language%22%3A%22en%22%2C%22collections%22%3A%5B%224WTWCSN6%22%2C%225EMCRXM5%22%5D%2C%22dateModified%22%3A%222025-07-09T14%3A45%3A18Z%22%7D%7D%5D%7D
Oñate-Socarras, M. K., Piñeres-Quiñones, O. H., Chen, L. M., Palecek, S. P., Lynn, D. M., & Acevedo-Vélez, C. (2026). Thermotropic liquid crystal droplets stabilized by nanoparticles for the optical detection of phospholipid membranes: impact of membrane composition on LC ordering transitions. Soft Matter. https://doi.org/10.1039/D5SM01253H
Yamagata, H. M., Padilla, M. S., Hamilton, A. G., Swingle, K. L., Thatte, A. S., Ricciardi, A. S., Agrawal, A., Fitzgerald, E., Poirier, A. J., Geisler, H. C., Joseph, R. A., Chalom, O. Z., Du, S., Li, J. J., Kim, D., Whitaker, R. C., Figueroa-Espada, C. G., Han, E. L., Murray, A. M., … Mitchell, M. J. (2026). Liver-Detargeted Aromatic Bioreducible mRNA Lipid Nanoparticles Confer Lymph Node Tropism and Robust Antigen-Specific Immunity. Journal of the American Chemical Society. https://doi.org/10.1021/jacs.6c00080
Mora-Navarro, C., Smith, E., Wang, Z., Ramos-Alamo, M. del C., Collins, L., Awad, N., Cruz, D. R. D., Tollison, T. S., Huntress, I., Gartling, G., Nakamura, R., Dion, G. R., Peng, X., Branski, R. C., & Freytes, D. O. (2026). Injection of vocal fold lamina propria-derived hydrogels modulates fibrosis in injured vocal folds. Biomaterials Advances, 178, 214424. https://doi.org/10.1016/j.bioadv.2025.214424
Buendia-Otero, M. J., Velez-Roman, L., Jimenez-Osorio, J., Ayus-Martinez, S., Meza-Morales, W., Domenech, M., Dion, G. R., Freytes, D., & Mora, C. (2025). Aerosolizable Formulations of Porcine Extracellular Matrix with Antibacterial and Immunomodulatory Effects. ACS Biomaterials Science & Engineering. https://doi.org/10.1021/acsbiomaterials.5c01304
Nakayama, L., & Zhou, L. (2025). In Vitro transcriptase: Methods for studying XNA synthesis. Methods in Enzymology. https://doi.org/10.1016/bs.mie.2025.10.010
Marzolini, N., Brysgel, T. V., Rahman, R. J., Essien, E.-O., Nwe, S. Y., Wu, J., Majumder, A., Patel, M. N., Tiwari, S., Espy, C. L., Dong, F., Shah, A., Shuvaev, V. V., Hood, E. D., Chase, L. S., Weissman, D., Katzen, J. B., Frank, D. B., Bennett, M. L., … Brenner, J. S. (2025). Targeting DNA-LNPs to Endothelial Cells Improves Expression Magnitude, Duration, and Specificity. bioRxiv. https://doi.org/10.1101/2025.07.09.663747
Padilla, M. S., Shepherd, S. J., Hanna, A. R., Kurnik, M., Zhang, X., Chen, M., Byrnes, J., Joseph, R. A., Yamagata, H. M., Ricciardi, A. S., Mrksich, K., Issadore, D., Gupta, K., & Mitchell, M. J. (2025). Elucidating lipid nanoparticle properties and structure through biophysical analyses. Nature Biotechnology, 1–14. https://doi.org/10.1038/s41587-025-02855-x
Wang, F., Qin, S., Yang, Z., Edwards-Medina, L. M., Chiu, B. L., Acevedo-Vélez, C., Remucal, C. K., Van Lehn, R. C., Zavala, V. M., & Lynn, D. M. (2025). A Machine Learning-Assisted Liquid Crystal Droplet Array Platform for the Sensitive and Selective Detection of Per- and Polyfluoroalkyl Substances (PFAS) in Water. ACS Sensors. https://doi.org/10.1021/acssensors.5c00907
Hanna, A. R., Issadore, D. A., & Mitchell, M. J. (2025). High-throughput platforms for machine learning-guided lipid nanoparticle design. Nature Reviews Materials, 1–15. https://doi.org/10.1038/s41578-025-00831-0
Dinh, H., Kegel, M., Melamed, J., Weissman, D., Stebe, K., & Lee, D. (2025). Room-Temperature Preservation of mRNA using Deep Eutectic Solvent. Chemistry. https://doi.org/10.26434/chemrxiv-2025-js4lr
Jones, H. T., Maus, N., Ludan, J. M., Huan, M. Z., Liang, J., Torres, M. D. T., Liang, J., Ives, Z., Barash, Y., Fuente-Nunez, C. de la, Gardner, J. R., & Yatskar, M. (2025). A Dataset for Distilling Knowledge Priors from Literature for Therapeutic Design (arXiv:2508.10899). arXiv. https://doi.org/10.48550/arXiv.2508.10899
Shuvaev, V. V., Tam, Y. K., Lee, B. W., Myerson, J. W., Herbst, A., Kiseleva, R. Yu., Glassman, P. M., Parhiz, H., Alameh, M.-G., Pardi, N., Muramatsu, H., Shuvaeva, T. I., Arguiri, E., Marcos-Contreras, O. A., Hood, E. D., Brysgel, T. V., Nong, J., Papp, T. E., Eaton, D. M., … Muzykantov, V. R. (2025). Systemic delivery of biotherapeutic RNA to the myocardium transiently modulates cardiac contractility in vivo. Proceedings of the National Academy of Sciences, 122(29), e2409266122. https://doi.org/10.1073/pnas.2409266122
Wang, S., Weissman, D., & Dong, Y. (2025). RNA chemistry and therapeutics. Nature Reviews Drug Discovery, 1–24. https://doi.org/10.1038/s41573-025-01237-x
Tao, J., Maus, N., Jones, H., Zeng, Y., Gardner, J. R., & Marcus, R. (2025). Learned Offline Query Planning via Bayesian Optimization. Proc. ACM Manag. Data, 3(3), 179:1-179:29. https://doi.org/10.1145/3725316
Maus, N., Kim, K., Pleiss, G., Eriksson, D., Cunningham, J. P., & Gardner, J. R. (2025). Approximation-aware Bayesian optimization. Proceedings of the 38th International Conference on Neural Information Processing Systems, NIPS ’24, 37, 21114–21140.
Wu, D., Maus, N., Jha, A., Yang, K., Wales-McGrath, B. D., Jewell, S., Tangiyan, A., Choi, P., Gardner, J. R., & Barash, Y. (2025). Generative modeling for RNA splicing predictions and design. eLife, 14. https://doi.org/10.7554/eLife.106043.1
Hwang, Y.-H., Shepherd, S. J., Kim, D., Mukalel, A. J., Mitchell, M. J., Issadore, D. A., & Lee, D. (2025). Robust, Scalable Microfluidic Manufacturing of RNA–Lipid Nanoparticles Using Immobilized Antifouling Lubricant Coating. ACS Nano, 19(1), 1090–1102. https://doi.org/10.1021/acsnano.4c12965
Razavi, R., Kegel, M., Muscat-Rivera, J., Weissman, D., & Melamed, J. R. (2025). Harnessing mRNA-lipid nanoparticles as innovative therapies for autoimmune diseases. Molecular Therapy Methods & Clinical Development, 101566. https://doi.org/10.1016/j.omtm.2025.101566
Torres, M. D. T., Zeng, Y., Wan, F., Maus, N., Gardner, J., & Fuente-Nunez, C. de la. (2024). A generative artificial intelligence approach for antibiotic optimization. bioRxiv. https://doi.org/10.1101/2024.11.27.625757
Wenger, J., Wu, K., Hennig, P., Gardner, J. R., Pleiss, G., & Cunningham, J. P. (2024). Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference (arXiv:2411.01036). arXiv. https://doi.org/10.48550/arXiv.2411.01036
Zhuang, F., Gutman, D., Islas, N., Guzman, B. B., Jimenez, A., Jewell, S., Hand, N. J., Nathanson, K., Dominguez, D., & Barash, Y. (2024). G4mer: An RNA language model for transcriptome-wide identification of G-quadruplexes and disease variants from population-scale genetic data. bioRxiv. https://doi.org/10.1101/2024.10.01.616124