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

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

9.6K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
9.6K
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

3.3K
3.3K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.2K
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...
15.2K
Cancer Survival Analysis01:21

Cancer Survival Analysis

588
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
588

You might also read

Related Articles

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

Sort by
Same author

Exploring Complex Genetic Mechanisms in Brain Imaging Genetics via a New Multi-task Learning Method.

IEEE transactions on computational biology and bioinformatics·2026
Same author

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026
Same author

Variations of global brain asymmetry are associated with aging and related diseases.

Science advances·2026
Same author

SpaVGMC: A Unified Representation Learning Framework via Structural and Semantic Alignment in Spatial Transcriptomics.

Journal of chemical information and modeling·2026
Same author

MHNNMDA: multi-stage hypergraph neural network for predicting miRNA-disease association types.

Journal of computer-aided molecular design·2026
Same author

Prediction of multicategory miRNA-disease associations based on bidirectional hypergraph attention network and gated convolutional strategy.

Journal of computer-aided molecular design·2026

Related Experiment Video

Updated: Dec 24, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

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

Published on: March 1, 2024

1.2K

WGRCMF: A Weighted Graph Regularized Collaborative Matrix Factorization Method for Predicting Novel LncRNA-Disease

Jin-Xing Liu, Zhen Cui, Ying-Lian Gao

    IEEE Journal of Biomedical and Health Informatics
    |April 15, 2020
    PubMed
    Summary

    Researchers developed a new method, weighted graph regularized collaborative matrix factorization (WGRCMF), to predict long non-coding RNA-disease associations (LDAs). This approach improves accuracy and identifies more potential LDAs, aiding disease research.

    More Related Videos

    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

    2.0K
    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

    1.9K

    Related Experiment Videos

    Last Updated: Dec 24, 2025

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
    03:37

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

    Published on: March 1, 2024

    1.2K
    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

    2.0K
    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

    1.9K

    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Numerous human diseases are linked to long non-coding RNAs (lncRNAs).
    • Discovering lncRNA-disease associations (LDAs) is challenging, time-consuming, and expensive.
    • Existing prediction methods for LDAs require improvement in accuracy and network construction.

    Purpose of the Study:

    • To propose an effective computational method for predicting novel lncRNA-disease associations (LDAs).
    • To enhance the accuracy of LDA prediction by addressing limitations in current network-based approaches.
    • To leverage manifold learning and matrix factorization for improved LDA discovery.

    Main Methods:

    • A novel weighted graph regularized collaborative matrix factorization (WGRCMF) method was developed.
    • Graph regularization terms and manifold learning were integrated into collaborative matrix factorization.
    • A weight matrix was introduced to mitigate the influence of unknown associations on predictions.

    Main Results:

    • The WGRCMF method demonstrated superior prediction accuracy compared to existing methods.
    • Simulation experiments on cancer-related LDAs confirmed the method's feasibility and effectiveness.
    • The approach successfully identified potential novel LDAs with higher precision.

    Conclusions:

    • The proposed WGRCMF method is a feasible and effective tool for predicting lncRNA-disease associations.
    • This method offers a significant advancement in identifying potential LDAs, aiding disease research and drug discovery.
    • Improved LDA prediction accuracy can accelerate the understanding of disease mechanisms and therapeutic targets.