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

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.

Nature communications - 16 Nov 2024

Murphy Alan E, Beardall William, Rei Marek, Phuycharoen Mike, Skene Nathan G

Abstract excerpt

Understanding how genetic variants affect the epigenome is key to interpreting GWAS, yet profiling these effects across the non-coding genome remains challenging due to experimental scalability. This necessitates accurate computational models. Existing machine learning approaches, while progressively improving, are confined to the cell types they were trained on, limiting their applicability. Here, we introduce...

Read the complete abstract on PubMed

Topics

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

Share to Chromatin
Share to Deep Learning
Share to DNA

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Epigenesis, Genetic

Share to Epigenome

Share to Epigenomics

Share to Models, Genetic

Share to Quantitative Trait Loci

Source

Open in PubMed Open full text

Identifiers

PMID: 39550354
DOI: 10.1038/s41467-024-54441-5
PMCID: PMC11569248

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.PMID 39550354Read ↗

Current genomic deep learning models display decreased performance in cell type specific accessible regionsSemantic proximity Read ↗

A sequence-based global map of regulatory activity for deciphering human geneticsSemantic proximity Read ↗

Mapping enhancer-gene regulatory interactions from single-cell dataSemantic proximity Read ↗

Cell type-specific interpretation of noncoding variants using deep learning-based methodsSemantic proximity Read ↗

scPrediXcan integrates advances in deep learning and single-cell data into a powerful cell-type–specific transcriptome-wide association study frameworkSemantic proximity Read ↗

An analytical framework for decoding cell type-specific genetic variation of gene regulation.Semantic proximity Read ↗

Harnessing machine learning models for epigenome to transcriptome association studiesSemantic proximity Read ↗

BayesCNet: Bayesian inference for cell type-specific regulatory networks leveraging cell type hierarchy in single-cell dataSemantic proximity Read ↗

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models.Semantic proximity Read ↗

In vivo measurement and prediction of cell-type-resolved non-coding variant effects in human immunitySemantic proximity Read ↗

CREP: Cis-Regulatory Element Predictor Based on Fine-Tuned EnformerSemantic proximity Read ↗

Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease allelesSemantic proximity Read ↗

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