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https://airfoundry.upenn.edu/wp-content/plugins/zotpress/
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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
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
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
Wenger, J., Wu, K., Hennig, P., Gardner, J. R., Pleiss, G., & Cunningham, J. P. (2025). Computation-aware gaussian processes: model selection and linear-time inference. Proceedings of the 38th International Conference on Neural Information Processing Systems, 37, 31316–31349.
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, 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
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 (No. 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