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Learning Biology Through Puzzle-solving: Unbiased Automatic Understanding of Microscopy Images with Self-supervised Learning

Published online by Cambridge University Press:  30 July 2020

Alex Lu ,
Oren Kraus ,
Sam Cooper  and
Alan Moses
Alex Lu
Affiliation:
University of Toronto, Toronto, Ontario, Canada
Oren Kraus
Affiliation:
Phenomic AI, Toronto, Ontario, Canada
Sam Cooper
Affiliation:
Phenomic AI, Toronto, Ontario, Canada
Alan Moses
Affiliation:
University of Toronto, Toronto, Ontario, Canada

Abstract

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Type
Advances in Modeling, Simulation, and Artificial Intelligence in Microscopy and Microanalysis for Physical and Biological Systems
Information
Microscopy and Microanalysis , Volume 26 , Supplement S2 , August 2020 , pp. 690 - 692
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Copyright
Copyright © Microscopy Society of America 2020

References

Uchida, S. Image processing and recognition for biological images. Dev. Growth Differ. 55, 523 (2013).10.1111/dgd.12054CrossRefGoogle ScholarPubMed
Bengio, Y., Courville, A. & Vincent, P. Representation Learning: A Review and New Perspectives. (2012).Google Scholar
Handfield, L.-F., Chong, Y. T., Simmons, J., Andrews, B. J. & Moses, A. M. Unsupervised clustering of subcellular protein expression patterns in high-throughput microscopy images reveals protein complexes and functional relationships between proteins. PLoS Comput. Biol. 9, e1003085 (2013).10.1371/journal.pcbi.1003085CrossRefGoogle ScholarPubMed
Li, Y., Majarian, T. D., Naik, A. W., Johnson, G. R. & Murphy, R. F. Point process models for localization and interdependence of punctate cellular structures. Cytom. Part A 89, 633–643 (2016).10.1002/cyto.a.22873CrossRefGoogle ScholarPubMed
Kraus, O. Z. et al. Automated analysis of high-content microscopy data with deep learning. Mol. Syst. Biol. 13, (2017).10.15252/msb.20177551CrossRefGoogle ScholarPubMed
Sullivan, D. P. et al. Deep learning is combined with massive-scale citizen science to improve large-scale image classification. Nat. Biotechnol. 36, 820–828 (2018).10.1038/nbt.4225CrossRefGoogle ScholarPubMed
Jing, L. & Tian, Y. Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey. (2019).Google Scholar
Lu, A. X., Kraus, O. Z., Cooper, S. & Moses, A. M. Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting. PLOS Comput. Biol. 15, e1007348 (2019).10.1371/journal.pcbi.1007348CrossRefGoogle ScholarPubMed
Chong, Y. T. et al. Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis. Cell 161, 1413–1424 (2015).10.1016/j.cell.2015.04.051CrossRefGoogle ScholarPubMed
Tkach, J. M. et al. Dissecting DNA damage response pathways by analysing protein localization and abundance changes during DNA replication stress. Nat. Cell Biol. 14, 966–76 (2012).10.1038/ncb2549CrossRefGoogle ScholarPubMed
Dubreuil, B. et al. YeastRGB: comparing the abundance and localization of yeast proteins across cells and libraries. Nucleic Acids Res. (2018) doi:10.1093/nar/gky941.Google Scholar
Thul, P. J. et al. A subcellular map of the human proteome. Science (80-.). 356, eaal3321 (2017).10.1126/science.aal3321CrossRefGoogle ScholarPubMed