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

RegVar: Tissue-specific Prioritization of Non-coding Regulatory Variants.

Genomics, proteomics & bioinformatics - 1 Apr 2023

Lu Hao, Ma Luyu, Quan Cheng, Li Lei, Lu Yiming, Zhou Gangqiao, Zhang Chenggang

Abstract excerpt

Non-coding genomic variants constitute the majority of trait-associated genome variations; however, the identification of functional non-coding variants is still a challenge in human genetics, and a method for systematically assessing the impact of regulatory variants on gene expression and linking these regulatory variants to potential target genes is still lacking. Here, we introduce a deep neural network...

Read the complete abstract on PubMed

Topics

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

Share to Genome-Wide Association Study
Share to Genomics
Share to Neural Networks, Computer

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Polymorphism, Single Nucleotide

Source

Open in PubMed Open full text

Identifiers

PMID: 34973416
DOI: 10.1016/j.gpb.2021.08.011
PMCID: PMC10626172

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

RegVar: Tissue-specific Prioritization of Non-coding Regulatory Variants.PMID 34973416Read ↗

RegVar: Tissue-specific Prioritization of Noncoding Regulatory VariantsSemantic proximity Read ↗

Principles and methods of in-silico prioritization of non-coding regulatory variants.Semantic proximity Read ↗

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.Semantic proximity Read ↗

GREEN-DB: A framework for the annotation and prioritization of non-coding regulatory variants from whole-genome sequencing dataSemantic proximity Read ↗

Prioritization of regulatory variants with tissue-specific function in the non-coding regions of human genomeSemantic proximity Read ↗

Organ-specific prioritization and annotation of non-coding regulatory variants in the human genomeSemantic proximity Read ↗

Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.Semantic proximity Read ↗

Whole genome base-wise aggregation and functional prediction for human non-coding regulatory variantsSemantic proximity Read ↗

Quantifying the regulatory potential of genetic variants via a hybrid sequence-oriented model with SVEN.Semantic proximity Read ↗

Predicting effects of noncoding variants with deep learning-based sequence model.Semantic proximity Read ↗

Classification of non-coding variants with high pathogenic impact.Semantic proximity Read ↗

Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory VariantsSemantic proximity Read ↗

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