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

Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning.

eLife - 15 Jun 2021

Liu Yi, Basty Nicolas, Whitcher Brandon, Bell Jimmy D, Sorokin Elena P, van Bruggen Nick, Thomas E Louise, Cule Madeleine

Abstract excerpt

Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective interventions. Magnetic resonance imaging (MRI) has been used to assess organ health, but biobank-scale studies are still in their infancy. Using over 38,000...

Read the complete abstract on PubMed

Topics

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

Share to Abdomen
Share to Adipose Tissue
Share to Body Composition

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Deep Learning

Share to Digestive System

Share to Image Processing, Computer-Assisted

Share to Magnetic Resonance Imaging

Source

Open in PubMed Open full text

Identifiers

PMID: 34128465
DOI: 10.7554/eLife.65554
PMCID: PMC8205492

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning.PMID 34128465Read ↗

Genetic architecture of 11 abdominal organ traits derived from abdominal MRI using deep learningSemantic proximity Read ↗

The role of liver fat in cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body compositionSemantic proximity Read ↗

The link between liver fat and cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body compositionSemantic proximity Read ↗

Unravelling genetic susceptibility and causal factors in liver health using MRI quantification of inflammation, fat and iron in the liver.Semantic proximity Read ↗

Machine learning across multiple imaging and biomarker modalities in the UK Biobank improves genetic discovery for liver fat accumulationSemantic proximity Read ↗

Machine learning enables new insights into genetic contributions to liver fat accumulationSemantic proximity Read ↗

Genome-wide association study of liver fat, iron, and extracellular fluid fraction in the UK BiobankSemantic proximity Read ↗

Genetic architecture of high-dimensional liver radiomic phenotypes and their role in common metabolic diseasesSemantic proximity Read ↗

Scaling genetic discovery for organ volumes using machine learning-assisted imputation and bias-corrected GWASSemantic proximity Read ↗

Genomic atlas of cardiac and adiposity imaging phenotypesSemantic proximity Read ↗

Linking abdominal imaging traits to electronic health record phenotypesSemantic proximity Read ↗

Multi-organ network of cardiometabolic disease-depression multimorbidity revealed by phenotypic and genetic analyses of MR images.Semantic proximity Read ↗

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