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

21.5K
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...
21.5K

You might also read

Related Articles

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

Sort by
Same author

Correction: Cell‑autonomous IL6ST activation suppresses prostate cancer development via STAT3/ARF/p53‑driven senescence and confers an immune‑active tumor microenvironment.

Molecular cancer·2026
Same author

Agentomics: an agentic system that autonomously develops novel state-of-the-art solutions for biomedical machine learning tasks.

Bioinformatics (Oxford, England)·2026
Same author

AlphaFind v2: similarity search in AlphaFold DB and TED domains across structural contexts.

Nucleic acids research·2026
Same author

SenCat: Cataloging human cell senescence through multiomic profiling of multiple senescent primary cell types.

bioRxiv : the preprint server for biology·2026
Same author

Repression of miR-29 via MYC leads to increased CD40 signaling in transformed follicular lymphoma.

Leukemia·2026
Same author

Senescence: An Overlooked VSMC Phenotype and Therapeutic Opportunity?

Arteriosclerosis, thrombosis, and vascular biology·2025

Related Experiment Video

Updated: Aug 16, 2025

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

2.6K

miRBind: A Deep Learning Method for miRNA Binding Classification.

Eva Klimentová1, Václav Hejret1,2, Ján Krčmář3

  • 1Central European Institute of Technology (CEITEC), Masaryk University, 60177 Brno, Czech Republic.

Genes
|December 23, 2022
PubMed
Summary

Predicting microRNA (miRNA) target binding is crucial for understanding gene regulation. miRBind, a novel deep learning approach, accurately identifies miRNA:target site interactions, outperforming existing seed-based and free energy methods.

Keywords:
CLASHconvolutional neural networkmiRNA bindingmiRNA:target prediction

More Related Videos

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs
08:49

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

7.7K
A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants
06:34

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Published on: January 21, 2020

8.4K

Related Experiment Videos

Last Updated: Aug 16, 2025

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

2.6K
Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs
08:49

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

7.7K
A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants
06:34

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Published on: January 21, 2020

8.4K

Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • MicroRNA (miRNA) binding to target sites is essential for gene regulation, mediated by Argonaute (Ago) proteins.
  • Current miRNA target prediction methods rely on 'seed' regions or free energy calculations, which are limited and often miss non-canonical interactions.

Purpose of the Study:

  • To develop and present miRBind, a deep learning-based method for accurate prediction of miRNA:target site binding potential.
  • To overcome the limitations of existing seed-based and free energy approaches in miRNA target prediction.

Main Methods:

  • Developed miRBind, a deep learning model trained on seed-agnostic experimental data.
  • Evaluated miRBind's performance against established seed-based and co-folding free energy prediction methods.
  • Created a freely accessible web server for the miRBind tool.

Main Results:

  • miRBind demonstrates superior accuracy in predicting miRNA:target site binding compared to traditional methods.
  • The deep learning approach effectively captures non-canonical binding interactions, which are prevalent in vivo.
  • Experimental data confirms the enhanced predictive power of the seed-agnostic model.

Conclusions:

  • miRBind offers a significant advancement in miRNA target prediction accuracy.
  • The method provides a more comprehensive understanding of miRNA-mediated gene regulation by accounting for non-canonical targets.
  • The availability of the miRBind web server facilitates broader research in miRNA function and disease.