Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

14.1K
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...
14.1K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

15.8K
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,...
15.8K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

17.9K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
17.9K

You might also read

Related Articles

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

Sort by
Same author

Preoperative risk stratification for long-term neurological status in spinal ependymoma: an MRI-centered nomogram.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·2026
Same author

Precision Engineered Dissolving Microneedles Enable Green-Light Activated Chemo-Photodynamic Therapy for Psoriasis.

ACS applied materials & interfaces·2026
Same author

The CGCG response assessment criteria for spinal cord gliomas.

Cancer letters·2026
Same author

Single-cell sequencing-guided design of synergistic chemo-immunotherapy nanodrugs for cGAS-STING activation in prostate cancer therapy.

Journal of nanobiotechnology·2026
Same author

A mouse model of classical trigeminal neuralgia via intradural compression of the trigeminal nerve.

The journal of headache and pain·2025
Same author

CABNas-nir: A near-infrared classification for urban pipe network sludge on the fusion algorithm of NAS framework and active learning.

PloS one·2025

Related Experiment Video

Updated: Sep 9, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K

GCNMF-SDA: predicting snoRNA-disease associations based on graph convolution and non-negative matrix factorization.

Yaowu Zhang1,2, Xiu Jin1,2, Xiaodan Zhang1,2

  • 1College of Information and Artificial Intelligence, Anhui Agricultural University, 130, Changjiang West Road, Hefei, Anhui Province 230036, China.

Briefings in Bioinformatics
|September 4, 2025
PubMed
Summary

We developed GCNMF-SDA, a computational method predicting small nucleolar RNA (snoRNA)-disease associations. This approach efficiently identifies potential links, aiding disease mechanism understanding and reducing experimental costs.

Keywords:
GCNWKNKNnon-negative matrix factorizationsimilarity network fusionsnoRNA-disease association

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.5K

Related Experiment Videos

Last Updated: Sep 9, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.5K

Area of Science:

  • * Molecular Biology
  • * Bioinformatics
  • * Computational Biology

Background:

  • * Small nucleolar RNAs (snoRNAs) are vital in biological processes, but their disease associations are underexplored.
  • * Traditional experimental methods for identifying snoRNA-disease links are costly and time-consuming.
  • * Efficient computational tools are needed to predict these associations.

Purpose of the Study:

  • * To propose a novel computational method, GCNMF-SDA, for predicting snoRNA-disease associations.
  • * To leverage multiple similarity data types for robust feature extraction.
  • * To enhance understanding of disease pathogenesis through predicted snoRNA involvement.

Main Methods:

  • * Integration of five similarity types for snoRNA and disease entities.
  • * Application of Similarity Network Fusion (SNF) and weighted K nearest known neighbors (WKNKN).
  • * Utilization of graph convolution and non-negative matrix factorization for feature extraction, followed by a multilayer perceptron classifier.

Main Results:

  • * GCNMF-SDA achieved high predictive performance with AUC-ROC of 0.9659 and AUC-PR of 0.9522.
  • * Rigorous five-fold cross-validation confirmed the model's efficacy.
  • * Case studies validated most novel snoRNA-disease associations predicted by the model.

Conclusions:

  • * GCNMF-SDA is a reliable and efficient computational tool for predicting snoRNA-disease associations.
  • * The method aids in uncovering novel biological insights into disease mechanisms.
  • * This approach can significantly accelerate research in molecular biology and disease pathogenesis.