Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Integrated multi-omics analyses reveal impaired energy homeostasis underlying tongue-rolling behavior in dairy cattle.

Veterinary journal (London, England : 1997)·2026

Same author

Chemically Programmable Liquid-to-Solid Transitions and Spatial Dynamics in Synthetic Coacervates.

Journal of the American Chemical Society·2026

Same author

Sequencing Ablation and Systemic Therapy in Colorectal Liver Oligometastases: An Upfront versus Delayed Approach Nationwide Analysis.

Radiology·2026

Same author

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026

Same author

CRISPRi-mediated silencing of the ABC transporter <i>Ms_5779</i> compromises cell envelope integrity and impairs intracellular host lipid exploitation.

Frontiers in microbiology·2026

Same author

Treatment of sleep disorders with traditional Chinese medicine: Pharmacological activities and mechanisms.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026

Same journal

Model-based quantification of protein-protein interaction aberrations for exploring dysregulated signalling pathways through pathway maps and gene expression levels.

BMC bioinformatics·2026

Same journal

Research on multi-trait genome association study method based on Shannon information entropy.

BMC bioinformatics·2026

Same journal

A multi-view feature fusion framework with interpretable graph convolution for predicting microbe-drug associations.

BMC bioinformatics·2026

Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026

Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026

Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 14, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Denoising self-supervised learning for disease-gene association prediction.

Yan Zhang^1,2, Ju Xiang³, Jianming Li⁴

¹School of Computer Science and Engineering, Central South University, Changsha, China.

BMC Bioinformatics

|October 23, 2025

Summary

This summary is machine-generated.

This study introduces DGSL, a novel denoising method for disease-gene association prediction. It captures crucial latent interactions and enhances self-supervised learning accuracy for better disease mechanism insights.

Keywords:

Denoising self-supervised learning Disease-gene associations prediction Similarity-guided

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Related Experiment Videos

Last Updated: Jan 14, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Understanding disease-gene interplay is vital for disease mechanisms and therapeutics.
Computational methods predict disease-gene associations, but face limitations.
Existing methods often overlook latent disease and gene neighbor interactions.

Purpose of the Study:

To propose a novel denoising method, DGSL, for disease-gene association prediction.
To address limitations in current computational approaches for disease-gene association prediction.
To improve the accuracy of modeling diseases and genes in self-supervised learning.

Main Methods:

Utilized bipartite graphs for diseases and genes to derive similarities.
Constructed disease and gene interaction graphs to capture latent patterns.
Implemented cross-view denoising with adaptive semantic alignment in embedding space.

Main Results:

The proposed DGSL method effectively captures valuable latent interaction patterns.
Cross-view denoising improved the accurate modeling of diseases and genes.
Extensive experiments validated the method's effectiveness on benchmark datasets.

Conclusions:

DGSL offers a novel approach to disease-gene association prediction.
The method enhances self-supervised learning by denoising and preserving neighbor interactions.
DGSL provides a more accurate framework for understanding disease-gene relationships.