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

Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes.

Bioinformatics (Oxford, England) - 19 Jul 2021

Nguyen Nam D, Jin Ting, Wang Daifeng

Abstract excerpt

SUMMARY: Population studies such as genome-wide association study have identified a variety of genomic variants associated with human diseases. To further understand potential mechanisms of disease variants, recent statistical methods associate functional omic data (e.g. gene expression) with genotype and phenotype and link variants to individual genes. However, how to interpret molecular mechanisms from such...

Read the complete abstract on PubMed

Topics

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

Share to Genome
Share to Genome-Wide Association Study
Share to Genomics

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genotype

Share to Neural Networks, Computer

Source

Open in PubMed Open full text

Identifiers

PMID: 33031552
DOI: 10.1093/bioinformatics/btaa866
PMCID: PMC8289382

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

Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes.PMID 33031552Read ↗

Varmole: A biologically drop-connect deep neural network model for prioritizing disease risk variants and genesSemantic proximity Read ↗

GenNet framework: interpretable deep learning for predicting phenotypes from genetic data.Semantic proximity Read ↗

Biologically-informed Interpretable Deep Learning Framework for Phenotype Prediction and Gene Interaction DetectionSemantic proximity Read ↗

GenNet framework: interpretable neural networks for phenotype predictionSemantic proximity Read ↗

Gene-centered representation of coding and regulatory variation enables outcome predictionSemantic proximity Read ↗

DeepSVP: integration of genotype and phenotype for structural variant prioritization using deep learning.Semantic proximity Read ↗

Modeling gene interactions in polygenic prediction via geometric deep learning.Semantic proximity Read ↗

Unifying the genetic landscape of common and rare diseases via latent neighborhoodsSemantic proximity Read ↗

RegVar: Tissue-specific Prioritization of Non-coding Regulatory Variants.Semantic proximity Read ↗

PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networksSemantic proximity Read ↗

DeepSVP: Integration of genotype and phenotype for structural variant prioritization using deep learningSemantic proximity Read ↗

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

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