Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

MicroRNAs

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...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

You might also read

Related Articles

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

Sort by

Same author

AviTag-seq unifies nucleotide-resolution maps of CRISPR off-targets and AAV vector integrations.

Communications biology·2026

Same author

TriPDCL: A Tri-Pathway Prototype-Driven Contrastive Learning Framework for Cross-Modality Single-Cell Integration.

Journal of chemical information and modeling·2026

Same author

DPSM-Synergy: A Dual-Path Feature Extraction and Synergy Matrix Enhancement Method for Anti-Cancer Drug Synergy Prediction.

Journal of chemical information and modeling·2026

Same author

Higher-Order Weighted Perturbation-Based Multilevel Information Fusion Model for Predicting CircRNA-Disease Associations.

Journal of chemical information and modeling·2025

Same author

IHDFN-DTI: Interpretable Hybrid Deep Feature Fusion Network for Drug-Target Interaction Prediction.

Interdisciplinary sciences, computational life sciences·2025

Same author

scMDCL: A Deep Collaborative Contrastive Learning Framework for Matched Single-Cell Multiomics Data Clustering.

Journal of chemical information and modeling·2025

Same journal

Literature-informed gene extraction and ranking for multimodal data fusion.

Briefings in bioinformatics·2026

Same journal

SA-MTP: a structure-aware framework for multifunctional therapeutic peptide annotation.

Briefings in bioinformatics·2026

Same journal

Genome assemblies and annotations are not static and need support for tracking their evolution.

Briefings in bioinformatics·2026

Same journal

A historical journey of metabolite-protein interaction discovery: from data harmonization to AI-driven prediction.

Briefings in bioinformatics·2026

Same journal

Bridging local-global transmembrane protein contexts with contrastive pretraining for alignment-free pathogenicity prediction.

Briefings in bioinformatics·2026

Same journal

Prediction of drug hypersensitivity by comprehensive modeling of HLA-peptidomes.

Briefings in bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 22, 2025

mirMachine: A One-Stop Shop for Plant miRNA Annotation

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

Generative Adversarial Matrix Completion Network based on Multi-Source Data Fusion for miRNA-Disease Associations

ShuDong Wang¹, YunYin Li¹, YuanYuan Zhang¹

¹College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), 66 Changjiang Xi Lu, 266580, Shandong, China.

Briefings in Bioinformatics

|July 23, 2023

Summary

This summary is machine-generated.

This study introduces GAMCNMDF, a novel computational model for predicting microRNA-disease associations by fusing diverse data sources. It demonstrates superior performance in identifying disease-related miRNAs and potential therapeutic targets.

Keywords:

generative adversarial nets matrix completion microRNA-disease associations similarity network

More Related Videos

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

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

Published on: October 4, 2019

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

Related Experiment Videos

Last Updated: Jul 22, 2025

mirMachine: A One-Stop Shop for Plant miRNA Annotation

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

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

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

Published on: October 4, 2019

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

Area of Science:

Biomedical Informatics
Computational Biology
Genomics

Background:

MicroRNAs (miRNAs) are crucial in complex diseases, serving as potential biomarkers and therapeutic targets.
Computational methods are increasingly used to identify disease-associated miRNAs.
Existing models face limitations due to insufficient data fusion and incomplete association knowledge.

Purpose of the Study:

To develop an advanced computational model for predicting miRNA-disease associations.
To overcome limitations of previous models in data fusion and handling incomplete information.
To enhance the accuracy and reliability of identifying disease-related miRNAs.

Main Methods:

Proposing Generative Adversarial Matrix Completion Network based on Multi-source Data Fusion (GAMCNMDF).
Integrating diverse data sources with a nonlinear fusion approach to update miRNA and disease similarity networks.
Utilizing a 'hint' mechanism to enable successful predictions with incomplete data.

Main Results:

GAMCNMDF demonstrated superior performance in 10-fold cross-validation on two databases.
The model achieved outstanding results in identifying small molecule-related miRNAs.
Case studies on neoplasms confirmed GAMCNMDF as a promising prediction tool.

Conclusions:

GAMCNMDF offers a robust and effective approach for miRNA-disease association prediction.
The model's ability to integrate diverse data and handle incomplete information enhances its applicability.
GAMCNMDF shows significant potential for advancing disease diagnosis and therapeutic target identification.