Predicting gene expression using morphological cell responses to nanotopography
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DOI: 10.1038/s41467-020-15114-1
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Cited by:
- Duy Pham & Xiao Tan & Brad Balderson & Jun Xu & Laura F. Grice & Sohye Yoon & Emily F. Willis & Minh Tran & Pui Yeng Lam & Arti Raghubar & Priyakshi Kalita-de Croft & Sunil Lakhani & Jana Vukovic & Ma, 2023. "Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues," Nature Communications, Nature, vol. 14(1), pages 1-25, December.
- Greta Simionato & Konrad Hinkelmann & Revaz Chachanidze & Paola Bianchi & Elisa Fermo & Richard van Wijk & Marc Leonetti & Christian Wagner & Lars Kaestner & Stephan Quint, 2021. "Red blood cell phenotyping from 3D confocal images using artificial neural networks," PLOS Computational Biology, Public Library of Science, vol. 17(5), pages 1-17, May.
- Lisa Laux & Marie F A Cutiongco & Nikolaj Gadegaard & Bjørn Sand Jensen, 2020. "Interactive machine learning for fast and robust cell profiling," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-16, September.
- John Arevalo & Ellen Su & Jessica D. Ewald & Robert Dijk & Anne E. Carpenter & Shantanu Singh, 2024. "Evaluating batch correction methods for image-based cell profiling," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
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