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

4.1K
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...
4.1K
MicroRNAs01:22

MicroRNAs

24.3K
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...
24.3K

You might also read

Related Articles

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

Sort by
Same author

KGLAR: Deconvoluting Spatial Transcriptomics Data with Single-cell Transcriptomes through Knowledge-guided NMF and Least Angle Regression.

Interdisciplinary sciences, computational life sciences·2026
Same author

Three-dimensional gelatin sponge culture potentiates MSC secretome to enhance full-thickness wound healing and induce hair follicle neogenesis via Wnt/β-catenin and TLR3/STAT3 activation in rats.

Acta histochemica·2026
Same author

Deciphering lncRNA-disease associations based on multi-representation fusion and boosting with Gaussian process.

IEEE journal of biomedical and health informatics·2026
Same author

CELLetter: leveraging large language model and dual-stream network to identify context-specific ligand-receptor interactions for cell-cell communication analysis.

Briefings in bioinformatics·2025
Same author

SGcCA: Deciphering Drug-Target Interactions through an End-to-End Model with Spatial and Channel Reconstruction Convolution and Cross-Efficient-Additive Attention.

Journal of chemical information and modeling·2025
Same author

PRDAGE: a prescription recommendation framework for traditional Chinese medicine based on data augmentation and multi-graph embedding.

PeerJ. Computer science·2025

Related Experiment Video

Updated: Feb 20, 2026

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

3.0K

NARRMDA: negative-aware and rating-based recommendation algorithm for miRNA-disease association prediction.

Lihong Peng1, Yeqing Chen, Ning Ma

  • 1College of Information Engineering, Changsha Medical University, Changsha, 410219, China.

Molecular Biosystems
|October 21, 2017
PubMed
Summary

A new computational model, NARRMDA, accurately predicts disease-related microRNAs (miRNAs) by integrating known associations and similarity measures. This method demonstrates superior predictive performance compared to existing models, aiding in disease research.

More Related Videos

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method
09:06

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method

Published on: October 7, 2025

435
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

2.9K

Related Experiment Videos

Last Updated: Feb 20, 2026

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

3.0K
MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method
09:06

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method

Published on: October 7, 2025

435
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

2.9K

Area of Science:

  • Biomedical Informatics
  • Genomics
  • Computational Biology

Background:

  • MicroRNAs (miRNAs) are crucial in biological processes and human diseases.
  • Predicting miRNA-disease associations is vital for disease research.
  • Existing computational methods for miRNA-disease prediction lack reliability.

Purpose of the Study:

  • To develop a novel computational model, NARRMDA, for predicting potential miRNA-disease associations.
  • To enhance the accuracy and reliability of miRNA-disease association predictions.
  • To provide a robust tool for identifying disease-related miRNAs.

Main Methods:

  • Developed the Negative-Aware and rating-based Recommendation algorithm for miRNA-Disease Association prediction (NARRMDA).
  • Utilized known miRNA-disease associations from the HMDD database.
  • Incorporated miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity.
  • Employed leave-one-out cross-validation for performance evaluation.

Main Results:

  • NARRMDA achieved a superior Area Under the Curve (AUC) of 0.8053, outperforming four classical prediction models.
  • Case studies on colon neoplasms, esophageal neoplasms, lymphoma, and breast neoplasms showed high validation rates (92%, 84%, 92%, 100% for top 50 predictions).
  • Experimental validation confirmed the reliable prediction ability of NARRMDA.

Conclusions:

  • NARRMDA demonstrates superior prediction accuracy and reliability for identifying disease-associated miRNAs.
  • The model offers a valuable computational approach for advancing miRNA-disease association research.
  • NARRMDA can significantly aid in the discovery of novel biomarkers and therapeutic targets.