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 disease-specific histone modifications and functional effects of non-coding variants by leveraging DNA language models.

Genome biology - 14 Feb 2026

Wang Xiaoyu, Pan Tong, Chen Sihan, Webb Geoffrey I, Jiang Yunzhe, Rozowsky Joel, Gerstein Mark, Song Jiangning

Abstract excerpt

BACKGROUND: Epigenetic modifications play a vital role in the pathogenesis of human diseases, particularly neurodegenerative disorders such as Alzheimer's disease, where dysregulated histone modifications are strongly implicated in disease mechanisms. While recent advances underscore the importance of accurately identifying these modifications to elucidate their contribution to Alzheimer's disease pathology,...

Read the complete abstract on PubMed

Topics

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

Share to Alzheimer Disease
Share to Deep Learning

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Epigenesis, Genetic

Share to Genetic Variation

Share to Histone Code

Share to Histones

Source

Open in PubMed Open full text

Identifiers

PMID: 41691336
DOI: 10.1186/s13059-026-04003-3
PMCID: PMC13011769

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Predicting disease-specific histone modifications and functional effects of non-coding variants by leveraging DNA language models.PMID 41691336Read ↗

Predicting Disease-Specific Histone Modifications and Functional Effects of Non-coding Variants by Leveraging DNA Language ModelsSemantic proximity Read ↗

Leveraging genomic large language models to enhance causal genotype-brain-clinical pathways in Alzheimer’s diseaseSemantic proximity Read ↗

Silencer variants are key drivers of gene up-regulation in Alzheimer's disease.Semantic proximity Read ↗

Functional Interpretation of Genetic Variants Using Deep Learning Predicts Impact on EpigenomeSemantic proximity Read ↗

A Network-based Deep Learning Framework Catalyzes GWAS and Multi-Omics Findings to Biology and Drug Repurposing for Alzheimer’s DiseaseSemantic proximity Read ↗

Human whole-epigenome modelling for clinical applications with PleiadesSemantic proximity Read ↗

Predicting the effect of non-coding mutations on single-cell DNA methylation using deep learningSemantic proximity Read ↗

DeepPerVar: a multimodal deep learning framework for functional interpretation of genetic variants in personal genomeSemantic proximity Read ↗

DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome.Semantic proximity Read ↗

Deep5hmC: Predicting genome-wide 5-Hydroxymethylcytosine landscape via a multimodal deep learning modelSemantic proximity Read ↗

Epigenetic genes are differentially methylated in the blood of persons with mild cognitive impairment and Alzheimer’s diseaseSemantic proximity Read ↗

The YTHDF proteins shape the brain gene signatures of Alzheimer’s diseaseSemantic proximity Read ↗

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