AI4Life

Outcomes

Starting its journey in September 2022 and scheduled to conclude in August 2025, the AI4Life project is dedicated to bringing together the life science and computational communities. As we navigate through this timeframe, we are compiling a comprehensive overview of the significant outcomes we’ve achieved so far.

Publications

Seifi, M., Nogare, D. D., Battagliotti, J., Galinova, V., Rao, A. K., AI4Life Horizon Europe Programme Consortium, Decelle, J., Jug, F., & Deschamps, J. (2024). FeatureForest: the power of foundation models, the usability of random forests. https://doi.org/10.1101/2024.12.12.628025
Sorensen, L., Humenick, A., Poon, S. S. B., Han, M. N., Mahdavian, N. S., Rowe, M. C., Hamnett, R., Gómez-de-Mariscal, E., Neckel, P. H., Saito, A., Mutunduwe, K., Glennan, C., Haase, R., McQuade, R. M., Foong, J. P. P., Brookes, S. J. H., Kaltschmidt, J. A., Muñoz-Barrutia, A., King, S. K., … Rajasekhar, P. (2024). Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. Journal of Cell Science, jcs.261950. https://doi.org/10.1242/jcs.261950
Lei, W., Fuster-Barceló, C., Reder, G., Muñoz-Barrutia, A., & Ouyang, W. (2024). BioImage.IO Chatbot: a community-driven AI assistant for integrative computational bioimaging. Nature Methods, 21(8), 1368–1370. https://doi.org/10.1038/s41592-024-02370-y
Hidalgo-Cenalmor, I., Pylvänäinen, J. W., G. Ferreira, M., Russell, C. T., Saguy, A., Arganda-Carreras, I., Shechtman, Y., AI4Life Horizon Europe Program Consortium, Muñoz-Barrutia, A., Serrano-Solano, B., Barcelo, C. F., Pape, C., Lundberg, E., Jug, F., Deschamps, J., Ferreira, M. G., Hartley, M., Seifi, M., Zulueta-Coarasa, T., … Gómez-de-Mariscal, E. (2024). DL4MicEverywhere: deep learning for microscopy made flexible, shareable and reproducible. Nature Methods. https://doi.org/10.1038/s41592-024-02295-6
Saguy, A., Nahimov, T., Lehrman, M., Gómez-de-Mariscal, E., Hidalgo-Cenalmor, I., Alalouf, O., Henriques, R., & Shechtman, Y. (2024). This microtubule does not exist: Super-resolution microscopy image generation by a diffusion model. bioRxiv. https://doi.org/10.1101/2023.07.06.548004
Franco-Barranco, D., Andrés-San Román, J. A., Hidalgo-Cenalmor, I., Backová, L., González-Marfil, A., Caporal, C., Chessel, A., Gómez-Gálvez, P., Escudero, L. M., Wei, D., Muñoz-Barrutia, A., & Arganda-Carreras, I. (2024). BiaPy: A unified framework for versatile bioimage analysis with deep learning [Preprint]. bioRxiv. https://doi.org/10.1101/2024.02.03.576026
Gómez-de-Mariscal, E., Del Rosario, M., Pylvänäinen, J. W., Jacquemet, G., & Henriques, R. (2024). Harnessing artificial intelligence to reduce phototoxicity in live imaging. Journal of Cell Science, 137(3), jcs261545. https://doi.org/10.1242/jcs.261545
García López De Haro, C., Dallongeville, S., Musset, T., Gómez-de-Mariscal, E., Sage, D., Ouyang, W., Muñoz-Barrutia, A., Tinevez, J.-Y., & Olivo-Marin, J.-C. (2024). JDLL: a library to run deep learning models on Java bioimage informatics platforms. Nature Methods, 21(1), 7–8. https://doi.org/10.1038/s41592-023-02129-x
Fuster-Barceló, C., García López De Haro, C., Gómez-de-Mariscal, E., Ouyang, W., Olivo-Marin, J.-C., Sage, D., & Muñoz-Barrutia, A. (2024). Bridging the Gap: Integrating Cutting-edge Techniques into Biological Imaging with deepImageJ [Preprint]. bioRxiv. https://doi.org/10.1101/2024.01.12.575015
Morgado, L., Gómez-de-Mariscal, E., Heil, H. S., & Henriques, R. (2024). The rise of data-driven microscopy powered by machine learning. Journal of Microscopy, 295(2), 85–92. https://doi.org/10.1111/jmi.13282
Pylvänäinen, J. W., Gómez-de-Mariscal, E., Henriques, R., & Jacquemet, G. (2023). Live-cell imaging in the deep learning era. Current Opinion in Cell Biology, 85, 102271. https://doi.org/10.1016/j.ceb.2023.102271
Laine, R. F., Heil, H. S., Coelho, S., Nixon-Abell, J., Jimenez, A., Wiesner, T., Martínez, D., Galgani, T., Régnier, L., Stubb, A., Follain, G., Webster, S., Goyette, J., Dauphin, A., Salles, A., Culley, S., Jacquemet, G., Hajj, B., Leterrier, C., & Henriques, R. (2023). High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation. Nature Methods, 20(12), 1949–1956. https://doi.org/10.1038/s41592-023-02057-w
Spahn, C., Middlemiss, S., Gómez-de-Mariscal, E., Henriques, R., Bode, H. B., Holden, S., & Heilemann, M. (2023). Transertion and cell geometry organize the Escherichia coli nucleoid during rapid growth [Preprint]. bioRxiv. https://doi.org/10.1101/2023.10.16.562172
Saraiva, B. M., Cunha, I. M., Brito, A. D., Follain, G., Portela, R., Haase, R., Pereira, P. M., Jacquemet, G., & Henriques, R. (2023). NanoPyx: super-fast bioimage analysis powered by adaptive machine learning [Preprint]. Bioinformatics. https://doi.org/10.1101/2023.08.13.553080
Kemmer, I., Keppler, A., Serrano-Solano, B., Rybina, A., Özdemir, B., Bischof, J., El Ghadraoui, A., Eriksson, J. E., & Mathur, A. (2023). Building a FAIR image data ecosystem for microscopy communities. Histochemistry and Cell Biology. https://doi.org/10.1007/s00418-023-02203-7
Maška, M., Ulman, V., Delgado-Rodriguez, P., Gómez-de-Mariscal, E., Nečasová, T., Guerrero Peña, F. A., Ren, T. I., Meyerowitz, E. M., Scherr, T., Löffler, K., Mikut, R., Guo, T., Wang, Y., Allebach, J. P., Bao, R., Al-Shakarji, N. M., Rahmon, G., Toubal, I. E., Palaniappan, K., … Ortiz-de-Solórzano, C. (2023). The Cell Tracking Challenge: 10 years of objective benchmarking. Nature Methods, 20(7), 1010–1020. https://doi.org/10.1038/s41592-023-01879-y
Volpe, G., Wählby, C., Tian, L., Hecht, M., Yakimovich, A., Monakhova, K., Waller, L., Sbalzarini, I. F., Metzler, C. A., Xie, M., Zhang, K., Lenton, I. C. D., Rubinsztein-Dunlop, H., Brunner, D., Bai, B., Ozcan, A., Midtvedt, D., Wang, H., Sladoje, N., … Bergman, J. (2023). Roadmap on Deep Learning for Microscopy (No. arXiv:2303.03793). arXiv. https://doi.org/10.48550/arXiv.2303.03793
Workshop participants. (2023). AI4Life & BioImage Archive FAIR AI Workshop - Summary of recommendations. https://doi.org/10.5281/zenodo.7681687
Ouyang, W., Eliceiri, K. W., & Cimini, B. A. (2023). Moving beyond the desktop: prospects for practical bioimage analysis via the web. Frontiers in Bioinformatics, 3. https://www.frontiersin.org/articles/10.3389/fbinf.2023.1233748
Ouyang, W., Beuttenmueller, F., Gómez-de-Mariscal, E., Pape, C., Burke, T., Garcia-López-de-Haro, C., Russell, C., Moya-Sans, L., de-la-Torre-Gutiérrez, C., Schmidt, D., Kutra, D., Novikov, M., Weigert, M., Schmidt, U., Bankhead, P., Jacquemet, G., Sage, D., Henriques, R., Muñoz-Barrutia, A., … Kreshuk, A. (2022). BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis. https://doi.org/10.1101/2022.06.07.495102