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

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

MVCL: A Contrastive Learning Model with Multi-view Networks for Driver Gene Prediction.

IEEE journal of biomedical and health informatics·2026
Same author

scDEBGCL: a deep embedding approach based on bipartite graph contrastive learning for single-cell RNA-seq data.

BMC biology·2026
Same author

scSCCNIA: similarity matrix based contrastive clustering with neighbor information aggregation for single-cell RNA sequencing data.

Briefings in bioinformatics·2026
Same author

DeepSGE: predicting spatial gene expression using residual network with efficient channel attention and dynamic graph attention network.

BMC genomics·2026

Related Experiment Video

Updated: Nov 2, 2025

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
10:27

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

1.8K

Double matrix completion for circRNA-disease association prediction.

Zong-Lan Zuo1, Rui-Fen Cao1,2, Pi-Jing Wei3

  • 1Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, China.

BMC Bioinformatics
|June 9, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a computational method, DMCCDA, to predict circular RNA (circRNA)-disease associations. The approach efficiently identifies potential circRNA-disease links, aiding experimental validation and advancing disease research.

Keywords:
Matrix completionSimilarity matrixcircRNA-disease associations

More Related Videos

Identification of Circular RNAs using RNA Sequencing
08:25

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

12.4K
CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis
10:40

CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

Published on: April 25, 2022

2.6K

Related Experiment Videos

Last Updated: Nov 2, 2025

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
10:27

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

1.8K
Identification of Circular RNAs using RNA Sequencing
08:25

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

12.4K
CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis
10:40

CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

Published on: April 25, 2022

2.6K

Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Circular RNAs (circRNAs) are RNA molecules with a closed-loop structure implicated in disease development.
  • Experimental validation of circRNA-disease associations is resource-intensive and time-consuming.
  • Developing efficient computational methods is crucial for prioritizing experimental investigations.

Purpose of the Study:

  • To propose a reliable computational method for predicting potential circRNA-disease associations.
  • To enhance the efficiency of biological experiments by identifying candidate circRNA-disease links.
  • To provide a tool for researchers studying the role of circRNAs in diseases.

Main Methods:

  • Developed a double matrix completion method (DMCCDA) for circRNA-disease association prediction.
  • Constructed circRNA and disease similarity matrices using sequence and semantic information.
  • Utilized Gaussian interaction profile similarity based on known circRNA-disease associations.
  • Employed matrix multiplication and completion techniques to update association and similarity matrices.

Main Results:

  • The DMCCDA model demonstrated strong performance in leave-one-out and five-fold cross-validation.
  • Case studies confirmed the effectiveness of the DMCCDA model in predicting circRNA-disease associations.
  • The method successfully identified potential circRNA candidates for specific diseases.

Conclusions:

  • The proposed DMCCDA method is effective for recommending potential circRNAs for diseases.
  • This computational approach can significantly improve the efficiency of biological experiments.
  • The findings contribute to a better understanding of circRNA functions in disease pathogenesis.