ReviewPhysical biology2021
Emerging machine learning approaches to phenotyping cellular motility and morphodynamics.
Review in Physical biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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
15 citing papers in PubMed.
- Beyond Antihistamines: How Biologic and Small-Molecule Therapies Are Transforming Chronic Spontaneous Urticaria Care in Adults.Clinical drug investigation · 2025Review
- Diffraction-informed deep learning for molecular-specific holograms of breast cancer cells.APL bioengineering · 2025Article
- A quantitative characterization of the heterogeneous response of glioblastoma U-87 MG cell line to temozolomide.Scientific reports · 2025Article
- Development of label-free cell tracking for discrimination of the heterogeneous mesenchymal migration.PloS one · 2025Article
- Spatiotemporal feature learning for actin dynamics.PloS one · 2025Article
- Interpretable Fine-Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Network representation of multicellular activity in pancreatic islets: Technical considerations for functional connectivity analysis.PLoS computational biology · 2024Article
- Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion.Molecular systems biology · 2024Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
- Methods and computational tools to study eukaryotic cell migrationFrontiers in cell and developmental biology · 2024Review
- Methods and Measures for Investigating Microscale Motility.Integrative and comparative biology · 2023Review
- Deep cross-modal feature learning applied to predict acutely decompensated heart failure using in-home collected electrocardiography and transthoracic bioimpedance.Artificial intelligence in medicine · 2023Article
- Unsupervised Contour Tracking of Live Cells by Mechanical and Cycle Consistency Losses.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2023Article
- A machine learning approach to discover migration modes and transition dynamics of heterogeneous dendritic cells.Frontiers in immunology · 2023Article
- Protocol for live cell image segmentation to profile cellular morphodynamics using MARS-Net.STAR protocols · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Cells respond heterogeneously to molecular and environmental perturbations. Phenotypic heterogeneity, wherein multiple phenotypes coexist in the same conditions, presents challenges when interpreting the observed heterogeneity. Advances in live cell microscopy allow researchers to acquire an unprecedented amount of live cell image data at high spatiotemporal resolutions. Phenotyping cellular dynamics, however, is a nontrivial task and requires machine learning (ML) approaches to discern phenotypic heterogeneity from live cell images. In recent years, ML has proven instrumental in biomedical research, allowing scientists to implement sophisticated computation in which computers learn and effectively perform specific analyses with minimal human instruction or intervention. In this review, we discuss how ML has been recently employed in the study of cell motility and morphodynamics to identify phenotypes from computer vision analysis. We focus on new approaches to extract and learn meaningful spatiotemporal features from complex live cell images for cellular and subcellular phenotyping.
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What Socratic holds
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 Socratic graph.