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

Multidimensional phenotyping predicts lifespan and quantifies health in Caenorhabditis elegans.

PLoS computational biology - 1 Jul 2020

Martineau Céline N, Brown André E X, Laurent Patrick

Abstract excerpt

Ageing affects a wide range of phenotypes at all scales, but an objective measure of ageing remains challenging, even in simple model organisms. To measure the ageing process, we characterized the sequence of alterations of multiple phenotypes at organismal scale. Hundreds of morphological, postural, and behavioral features were extracted from high-resolution videos. Out of the 1019 features extracted, 896 are...

Read the complete abstract on PubMed

Topics

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

Share to Behavior, Animal
Share to Caenorhabditis elegans
Share to Caenorhabditis elegans Proteins

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Longevity

Share to Oxidative Stress

Share to Regression Analysis

Share to Stress, Mechanical

Share to Video Recording

Source

Open in PubMed Open full text

Identifiers

PMID: 32692770
DOI: 10.1371/journal.pcbi.1008002
PMCID: PMC7394451

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

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

Multidimensional phenotyping predicts lifespan and quantifies health in Caenorhabditis elegans.PMID 32692770Read ↗

Identification by machine vision of the rate of motor activity decline as a lifespan predictor in C. elegans.Semantic proximity Read ↗

Deep behavioural phenotyping reveals divergent trajectories of ageing and quantifies health state in <i>C. elegans</i>Semantic proximity Read ↗

Transcriptome States Reflect Imaging of Aging States.Semantic proximity Read ↗

Machine learning predicts lifespan and underlying causes of death in aging C. elegansSemantic proximity Read ↗

Machine learning predicts lifespan and underlying causes of death in aging <i>C. elegans</i>Semantic proximity Read ↗

Rapid measurement of ageing by automated monitoring of movement of C. elegans populationsSemantic proximity Read ↗

Quantitative Image Analysis Reveals Distinct Structural Transitions during Aging in Caenorhabditis elegans TissuesSemantic proximity Read ↗

Measurements of age-related changes of physiological processes that predict lifespan of Caenorhabditis elegans.Semantic proximity Read ↗

Two forms of death in ageing Caenorhabditis elegans.Semantic proximity Read ↗

Prediction of biological age by morphological staging of sarcopenia in <i>Caenorhabditis elegans</i>Semantic proximity Read ↗

Assessing Health Span in Caenorhabditis elegans: Lessons From Short-Lived Mutants.Semantic proximity Read ↗

Surveying Low-Cost Methods to Measure Lifespan and Healthspan in Caenorhabditis elegans.Semantic proximity Read ↗

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