Learning dynamic image representations for self-supervised cell cycle annotation Article Swipe
Kristina Ulicna
,
Manasi Kelkar
,
Christopher J. Soelistyo
,
Guillaume Charras
,
Alan R. Lowe
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.05.30.542796
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.05.30.542796
Time-based comparisons of single-cell trajectories are challenging due to their intrinsic heterogeneity, autonomous decisions, dynamic transitions and unequal lengths. In this paper, we present a self-supervised framework combining an image autoencoder with dynamic time series analysis of latent feature space to represent, compare and annotate cell cycle phases across singlecell trajectories. In our fully data-driven approach, we map similarities between heterogeneous cell tracks and generate statistical representations of single-cell trajectory phase durations, onset and transitions. This work is a first effort to transform a sequence of learned image representations from cell cycle-specific reporters into an unsupervised sequence annotation.
Related Topics
Concepts
Autoencoder
Computer science
Artificial intelligence
Sequence (biology)
Image (mathematics)
Annotation
Feature (linguistics)
Pattern recognition (psychology)
Unsupervised learning
Trajectory
Space (punctuation)
Feature vector
Machine learning
Computer vision
Deep learning
Genetics
Physics
Philosophy
Linguistics
Operating system
Astronomy
Biology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.05.30.542796
- https://www.biorxiv.org/content/biorxiv/early/2023/05/31/2023.05.30.542796.full.pdf
- OA Status
- green
- Cited By
- 11
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378803559
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4378803559Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.05.30.542796Digital Object Identifier
- Title
-
Learning dynamic image representations for self-supervised cell cycle annotationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-31Full publication date if available
- Authors
-
Kristina Ulicna, Manasi Kelkar, Christopher J. Soelistyo, Guillaume Charras, Alan R. LoweList of authors in order
- Landing page
-
https://doi.org/10.1101/2023.05.30.542796Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2023/05/31/2023.05.30.542796.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2023/05/31/2023.05.30.542796.full.pdfDirect OA link when available
- Concepts
-
Autoencoder, Computer science, Artificial intelligence, Sequence (biology), Image (mathematics), Annotation, Feature (linguistics), Pattern recognition (psychology), Unsupervised learning, Trajectory, Space (punctuation), Feature vector, Machine learning, Computer vision, Deep learning, Genetics, Physics, Philosophy, Linguistics, Operating system, Astronomy, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 8Per-year citation counts (last 5 years)
- References (count)
-
15Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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