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

MicroRNAs01:22

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...

You might also read

Related Articles

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

Sort by
Same author

Computational Method Using Attribute-Aware Message Passing and Graph Convolutional Network for Potential miRNA-Disease Association Prediction.

International journal of molecular sciences·2026
Same author

De-identification of clinical notes with pseudo-labeling using regular expression rules and pre-trained BERT.

BMC medical informatics and decision making·2025
Same author

Memristor Crossbar Circuits Implementing Equilibrium Propagation for On-Device Learning.

Micromachines·2023
Same author

Area-Efficient Mapping of Convolutional Neural Networks to Memristor Crossbars Using Sub-Image Partitioning.

Micromachines·2023
Same author

Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA-disease association prediction.

BMC bioinformatics·2022
Same author

Synapse-Neuron-Aware Training Scheme of Defect-Tolerant Neural Networks with Defective Memristor Crossbars.

Micromachines·2022

Related Experiment Video

Updated: Jul 14, 2026

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
09:40

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Published on: October 4, 2019

5.9K

Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model.

Lei Zhang1,2, Bailong Liu3,4, Zhengwei Li5,6

  • 1Engineering Research Center of Mine Digitalization of Ministry of Education, China University of Mining and Technology, Xuzhou, China.

BMC Bioinformatics
|October 22, 2020
PubMed
Summary

This study introduces M2GMDA, a novel graph embedding model that efficiently predicts microRNA-disease associations. The model significantly outperforms existing methods, aiding in disease diagnosis and treatment development.

Keywords:
Graph embeddingMeta-pathmiRNA-disease associations

More Related Videos

mirMachine: A One-Stop Shop for Plant miRNA Annotation
06:16

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

2.8K
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

Related Experiment Videos

Last Updated: Jul 14, 2026

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
09:40

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Published on: October 4, 2019

5.9K
mirMachine: A One-Stop Shop for Plant miRNA Annotation
06:16

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

2.8K
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

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • MicroRNAs (miRNAs) play crucial roles in human diseases, but experimental identification of miRNA-disease associations is costly and time-consuming.
  • Computational models offer an efficient alternative for predicting these associations, though improvements are still needed.

Purpose of the Study:

  • To develop an efficient computational model for predicting unidentified miRNA-disease associations.
  • To leverage complex network structures and semantic information for enhanced prediction accuracy.

Main Methods:

  • A multiple meta-paths fusion graph embedding model (M2GMDA) was developed.
  • A miRNA-disease heterogeneous network was constructed using verified pairs, miRNA similarity, and disease similarity.
  • Meta-path instances and attention mechanisms were integrated to capture rich interaction information.

Main Results:

  • M2GMDA achieved high AUC values of 0.9323 and 0.9182 in cross-validation.
  • Case studies on various neoplasms and lymphoma showed high validation rates for top predicted miRNA-disease associations.
  • The model demonstrated superior performance compared to existing prediction methods.

Conclusions:

  • M2GMDA effectively predicts miRNA-disease associations, confirming its strong performance.
  • The model's accuracy aids in identifying potential diagnostic and therapeutic targets.
  • The study provides a valuable tool for advancing miRNA-based disease research.