ArticleNature methods2012
Unsupervised modeling of cell morphology dynamics for time-lapse microscopy.
Article in Nature methods, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed.
- Development of a universal imaging "phenome" using shape, appearance and motion (SAM) features and the SAM Phenotype Observation Tool (SPOT).Nature communications · 2026Article
- Predicting cell cycle stage from 3D single-cell nuclear-stained images.Life science alliance · 2025Article
- Exploring heterogeneous cell population dynamics in different microenvironments by novel analytical strategy based on images.NPJ systems biology and applications · 2024Article
- Article
- Self-supervised machine learning for live cell imagery segmentation.Communications biology · 2022Article
- Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology.Physical biology · 2022Review
- LiveCellMiner: A new tool to analyze mitotic progression.PloS one · 2022Article
- Emerging machine learning approaches to phenotyping cellular motility and morphodynamics.Physical biology · 2021Review
- Live-cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data.Science advances · 2020Article
- Article
- Analyzing temporal dynamics of cell deformation and intracellular movement with video feature aggregation.Biomedical engineering online · 2019Article
- Superpixel-Based Conditional Random Fields (SuperCRF): Incorporating Global and Local Context for Enhanced Deep Learning in Melanoma Histopathology.Frontiers in oncology · 2019Article
- Computational Analysis of Cell Dynamics in Videos with Hierarchical-Pooled Deep-Convolutional Features.Journal of computational biology : a journal of computational molecular cell biology · 2018Article
- Article
- Morphometrics of complex cell shapes: lobe contribution elliptic Fourier analysis (LOCO-EFA).Development (Cambridge, England) · 2018Article
- Image-Based Tracking of Heterogeneous Single-Cell Phenotypes.Methods in molecular biology (Clifton, N.J.) · 2018Article
- Digging deep into Golgi phenotypic diversity with unsupervised machine learning.Molecular biology of the cell · 2017Article
- A deep learning and novelty detection framework for rapid phenotyping in high-content screening.Molecular biology of the cell · 2017Article
- Microfluidic device enabled quantitative time-lapse microscopic-photography for phenotyping vegetative and reproductive phases in Fusarium virguliforme, which is pathogenic to soybean.Scientific reports · 2017Article
- A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients.Scientific data · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Analysis of cellular phenotypes in large imaging data sets conventionally involves supervised statistical methods, which require user-annotated training data. This paper introduces an unsupervised learning method, based on temporally constrained combinatorial clustering, for automatic prediction of cell morphology classes in time-resolved images. We applied the unsupervised method to diverse fluorescent markers and screening data and validated accurate classification of human cell phenotypes, demonstrating fully objective data labeling in image-based systems biology.
Indexed as
Identifiers
22635062What OpenQuestion 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 OpenQuestion graph.