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

Self-supervised classification of subcellular morphometric phenotypes reveals extracellular matrix-specific morphological responses.

Scientific reports - 12 Sept 2022

Wong Kin Sun, Zhong Xueying, Low Christine Siok Lan, Kanchanawong Pakorn

Abstract excerpt

Cell morphology is profoundly influenced by cellular interactions with microenvironmental factors such as the extracellular matrix (ECM). Upon adhesion to specific ECM, various cell types are known to exhibit different but distinctive morphologies, suggesting that ECM-dependent cell morphological responses may harbour rich information on cellular signalling states. However, the inherent morphological complexity...

Read the complete abstract on PubMed

Topics

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

Share to Extracellular Matrix
Share to Fibroblasts

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Microscopy, Fluorescence

Share to Neural Networks, Computer

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 36097150
DOI: 10.1038/s41598-022-19472-2
PMCID: PMC9468179

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

Self-supervised classification of subcellular morphometric phenotypes reveals extracellular matrix-specific morphological responses.PMID 36097150Read ↗

Generalizable Morphological Profiling of Cells by Interpretable Unsupervised LearningSemantic proximity Read ↗

Subcellular Region Morphology Reflects Cellular IdentitySemantic proximity Read ↗

3D single-cell shape analysis using geometric deep learningSemantic proximity Read ↗

Learning unsupervised feature representations for single cell microscopy images with paired cell inpaintingSemantic proximity Read ↗

Cell shapes decode molecular phenotypes in image-based spatial proteomicsSemantic proximity Read ↗

Image-based Cell Phenotyping Using Deep LearningSemantic proximity Read ↗

Environmental properties of cells improve machine learning-based phenotype recognition accuracy.Semantic proximity Read ↗

Deep learning identifies heterogeneous subpopulations in breast cancer cell linesSemantic proximity Read ↗

Image-based phenotyping of disaggregated cells using deep learning.Semantic proximity Read ↗

Interpretable Fine-Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning.Semantic proximity Read ↗

Morphology-robust quantification of subcellular organization in complex cellsSemantic proximity Read ↗

Unsupervised phenotypic analysis of cellular images with multi-scale convolutional neural networksSemantic proximity Read ↗

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