ArticleScientific reports2020
Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 63 citations in OpenAlex.
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- Microengineering the Liver: Strategies for Constructing Functional Liver-on-a-Chip Devices.Exploration (Beijing, China) · 2026Review
- Autonomous microfluidic labs: progress and prospects.Lab on a chip · 2026Review
- Liver-on-a-Chip (LoC) Models: Case Studies of Academic Platforms and Commercial Products.Molecular pharmaceutics · 2026Review
- Functional material probes and advanced technologies in organ-on-a-chip characterization.Theranostics · 2026Review
- The Multifaceted Role of p53 in Cancer Molecular Biology: Insights for Precision Diagnosis and Therapeutic Breakthroughs.Biomolecules · 2025Review
- Cancer-on-a-chip for precision cancer medicine.Lab on a chip · 2025Review
- Review
- High-throughput solutions in tumor organoids: from culture to drug screening.Stem cells (Dayton, Ohio) · 2025Review
- Bridging the gap: how patient-derived lung cancer organoids are transforming personalized medicine.Frontiers in cell and developmental biology · 2025Review
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
- Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024Review
- Assessing personalized responses to anti-PD-1 treatment using patient-derived lung tumor-on-chip.Cell reports. Medicine · 2024Article
- Vascularized organoid-on-a-chip: design, imaging, and analysis.Angiogenesis · 2024Review
- MotGen: a closed-loop bacterial motility control framework using generative adversarial networks.Bioinformatics (Oxford, England) · 2024Article
- Classification of T lymphocyte motility behaviors using a machine learning approach.PLoS computational biology · 2023Article
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review.Biosensors · 2023Review
- Patient-derived organoids of lung cancer based on organoids-on-a-chip: enhancing clinical and translational applications.Frontiers in bioengineering and biotechnology · 2023Review
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We describe a novel method to achieve a universal, massive, and fully automated analysis of cell motility behaviours, starting from time-lapse microscopy images. The approach was inspired by the recent successes in application of machine learning for style recognition in paintings and artistic style transfer. The originality of the method relies i) on the generation of atlas from the collection of single-cell trajectories in order to visually encode the multiple descriptors of cell motility, and ii) on the application of pre-trained Deep Learning Convolutional Neural Network architecture in order to extract relevant features to be used for classification tasks from this visual atlas. Validation tests were conducted on two different cell motility scenarios: 1) a 3D biomimetic gels of immune cells, co-cultured with breast cancer cells in organ-on-chip devices, upon treatment with an immunotherapy drug; 2) Petri dishes of clustered prostate cancer cells, upon treatment with a chemotherapy drug. For each scenario, single-cell trajectories are very accurately classified according to the presence or not of the drugs. This original approach demonstrates the existence of universal features in cell motility (a so called "motility style") which are identified by the DL approach in the rationale of discovering the unknown message in cell trajectories.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.