Pattern recognition and classification of images are key challenges throughout the life sciences. We combined two approaches for large-scale classification of fluorescence microscopy images. First, using the publicly available data set from the Cell Atlas of the Human Protein Atlas (HPA), we integrated an image-classification task into a mainstream video game (EVE Online) as a mini-game, named Project Discovery. Participation by 322,006 gamers over 1 year provided nearly 33 million classifications of subcellular localization patterns, including patterns that were not previously annotated by the HPA. Second, we used deep learning to build an automated Localization Cellular Annotation Tool (Loc-CAT). This tool classifies proteins into 29 subcellular localization patterns and can deal efficiently with multi-localization proteins, performing robustly across different cell types. Combining the annotations of gamers and deep learning, we applied transfer learning to create a boosted learner that can characterize subcellular protein distribution with F1 score of 0.72. We found that engaging players of commercial computer games provided data that augmented deep learning and enabled scalable and readily improved image classification.

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We acknowledge the staff of the Human Protein Atlas program for valuable contributions. We acknowledge the EVE Development team, the University of Reykjavik and the University of Iceland for assistance with the game implementation. We acknowledge MMOS Sarl for serving images and managing response collection and CCP hf and MMOS Sarl for financially supporting the image storage and serving throughout Project Discovery. Funding to E.L. was provided by the Knut and Alice Wallenberg Foundation.

Author information

Author notes

    • Devin P Sullivan
    •  & Casper F Winsnes

    These authors contributed equally to this work.


  1. Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden.

    • Devin P Sullivan
    • , Casper F Winsnes
    • , Lovisa Åkesson
    • , Martin Hjelmare
    • , Mikaela Wiking
    • , Rutger Schutten
    •  & Emma Lundberg
  2. CCP hf, Reyjkavik, Iceland.

    • Linzi Campbell
    • , Hjalti Leifsson
    • , Scott Rhodes
    • , Andie Nordgren
    •  & Bergur Finnbogason
  3. Science for Life Laboratory, School of Computer Science and Communication, KTH - Royal Institute of Technology, Stockholm, Sweden.

    • Kevin Smith
  4. MMOS Sàrl, Monthey, Switzerland.

    • Bernard Revaz
    •  & Attila Szantner
  5. Department of Genetics, Stanford University, Stanford, California, USA.

    • Emma Lundberg
  6. Chan Zuckerberg Biohub, San Francisco, San Francisco, California, USA.

    • Emma Lundberg


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A.S., B.R., B.F., A.N. and E.L. conceived the study. M.H., A.S., B.F., E.L., D.P.S. and C.F.W. developed the methodology for the study. A.S. and B.R. developed the citizen science engine. L.C., H.L., S.R. and B.F. developed the game narrative and implementation. Project Discovery was played by thousands of players of EVE Online. D.P.S., L.Å., M.W., R.S. and E.L. provided game support. C.F.W., K.S. and D.P.S. developed the machine learning. D.P.S., C.F.W. and E.L. carried out data analysis and investigation. D.P.S., C.F.W. and E.L. wrote the manuscript. D.P.S. and C.F.W. created the figures. E.L. supervised and administered the project and acquired funding.

Competing interests

A.S. and B.R. are founders of MMOS Sarl.

Corresponding author

Correspondence to Emma Lundberg.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–5

  2. 2.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Table 1

    Comparison of protein subcellular localization methods from fluorescent microscopy images

  2. 2.

    Supplementary Table 2

    Project Discovery optimized per-class cutoffs

  3. 3.

    Supplementary Table 3

    Rods & Rings localized proteins found by Project Discovery

  4. 4.

    Supplementary Table 4

    Loc-CAT optimized per-class cutoffs

  5. 5.

    Supplementary Data Set 2

    SLF feature names used in Loc-CAT DNN

Zip files

  1. 1.

    Supplementary Data Set 1

    HPA version 14 “gold standard” annotations

Text files

  1. 1.

    Supplementary Data Set 3

    Expert reannotation results

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