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

Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies.

PLoS computational biology - 1 Dec 2021

Nguyen Phuc, Chien Sylvia, Dai Jin, Monnat Raymond J, Becker Pamela S, Kueh Hao Yuan

Abstract excerpt

Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static phenotypic readouts. Transmitted light images, on the other hand, provide direct morphological readouts and can be acquired over time to provide a rich data source...

Read the complete abstract on PubMed

Topics

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

Share to Blood Cells
Share to Image Processing, Computer-Assisted

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Leukemia, Myeloid, Acute

Share to Microscopy

Share to Single-Cell Analysis

Share to Time-Lapse Imaging

Share to Unsupervised Machine Learning

Source

Open in PubMed Open full text

Identifiers

PMID: 34968384
DOI: 10.1371/journal.pcbi.1009626
PMCID: PMC8754342

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

Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies.PMID 34968384Read ↗

Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity.Semantic proximity Read ↗

inVAE: Conditionally invariant representation learning for generating multivariate single-cell reference mapsSemantic proximity Read ↗

Morphological fingerprints enable machine learning based inference of neuroblastoma cell states without transcriptomicsSemantic proximity Read ↗

Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomesSemantic proximity Read ↗

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

Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon.Semantic proximity Read ↗

Longitudinal whole transcriptomic profiling of live cells through domain adaptationSemantic proximity Read ↗

Unified fate mapping in multiview single-cell dataSemantic proximity Read ↗

DynaMorph: self-supervised learning of morphodynamic states of live cellsSemantic proximity Read ↗

CellRank 2: unified fate mapping in multiview single-cell dataSemantic proximity Read ↗

Learning dynamics by computational integration of single cell genomic and lineage informationSemantic proximity Read ↗

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