Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.

Integrative biology : quantitative biosciences from nano to macro - 1 Jan 2016

Gordonov Simon, Hwang Mun Kyung, Wells Alan, Gertler Frank B, Lauffenburger Douglas A, Bathe Mark

Abstract excerpt

Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Cell Size

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Computer Simulation

Share to Image Interpretation, Computer-Assisted

Share to Models, Statistical

Share to Pattern Recognition, Automated

Share to Reproducibility of Results

Share to Sensitivity and Specificity

Source

Open in PubMed Open full text

Identifiers

PMID: 26658688
DOI: 10.1039/c5ib00283d
PMCID: PMC5058786

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.PMID 26658688Read ↗

CellPhe: a toolkit for cell phenotyping using time-lapse imaging and pattern recognitionSemantic proximity Read ↗

CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging.Semantic proximity Read ↗

Mining of Single-Cell Signaling Time-Series for Dynamic Phenotypes with Clustering.Semantic proximity Read ↗

Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state changeSemantic proximity Read ↗

Development of a universal imaging "phenome" using shape, appearance and motion (SAM) features and the SAM Phenotype Observation Tool (SPOT).Semantic proximity Read ↗

Image-Based Tracking of Heterogeneous Single-Cell Phenotypes.Semantic proximity Read ↗

High accuracy label-free classification of kinetic cell states from holographic cytometrySemantic proximity Read ↗

Scalable longitudinal imaging and transcriptomics of cells in dynamic enclosuresSemantic proximity Read ↗

IMAGENE: Single-cell association of live cell imaging and gene expression profiles of non-adherent cells through photoactivatable adhesivesSemantic proximity Read ↗

SPACe: an open-source, single-cell analysis of Cell Painting data.Semantic proximity Read ↗

Comparison of methods for image-based profiling of cellular morphological responses to small-molecule treatment.Semantic proximity Read ↗

ARTEMIS integrates autoencoders and Schrödinger Bridges to predict continuous dynamics of gene expression, cell population and perturbation from time-series single-cell dataSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.