Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • News & Views
  • Published:

Particle tracking

Geometric deep learning of particle motion by MAGIK

Nature Machine Intelligence volume 5, pages 483–484 (2023)Cite this article

Subjects

A new geometric deep learning method can reconstruct cellular and subcellular trajectories and characterize mobility in microscopic imaging, for a broad range of challenging scenarios.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: MAGIK pipeline from images to particle tracks or motion properties.

References

  1. Liu, Z., Lavis, L. D. & Betzig, E. Mol Cell 58, 644–659 (2015).

    Article  Google Scholar 

  2. Stelzer, E. H. K. et al. Nat. Rev. Methods Primers 1, 73 (2021).

    Article  Google Scholar 

  3. Pineda, J. et al. Nat. Mach. Intell. 5, 71–82 (2023).

    Article  Google Scholar 

  4. Jaqaman, K. et al. Nat. Methods 8, 695–702 (2008).

    Article  Google Scholar 

  5. Parutto, P. et al. Cell. Rep. Methods 2, 100277 (2022).

    Article  Google Scholar 

  6. Veličković, P. Curr. Opin. Struct. Biol. 79, 102538 (2023).

    Article  Google Scholar 

  7. Chami, I., Abu-El-Haija, S., Perozzi, B., Ré, C. & Murphy, K. J. Mach. Learn. Res. 23, 89 (2022).

    Google Scholar 

  8. Fatemi, B., El Asri, L. & Kazemi, S. M. In Advances in Neural Information Processing Systems 34, 22667–22681 (2021).

    Google Scholar 

  9. Fatemi, B., Taslakian, P., Vazquez, V. & Poole, D. Proc. IJCAI 29, 2191–2197 (2020).

    Google Scholar 

  10. Kazemi, S. M. In Graph Neural Networks: Foundations, Frontiers, and Applications (eds. Wu, L., Cui, P., Pei, J. & Zhao, L.) 323–349 (Springer, 2022).

Download references

Author information

Authors and Affiliations

  1. Google Montreal, Montreal, Quebec, Canada

    Bahare Fatemi

  2. Google Atlanta, Atlanta, GA, USA

    Jonathan Halcrow

  3. Department of Biophysics and Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Khuloud Jaqaman

Authors
  1. Bahare Fatemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonathan Halcrow
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khuloud Jaqaman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Bahare Fatemi, Jonathan Halcrow or Khuloud Jaqaman.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fatemi, B., Halcrow, J. & Jaqaman, K. Geometric deep learning of particle motion by MAGIK. Nat Mach Intell 5, 483–484 (2023). https://doi.org/10.1038/s42256-023-00660-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42256-023-00660-2

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics