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

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

8.8K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
8.8K
RNA Interference01:23

RNA Interference

26.3K
RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
26.3K
MicroRNAs01:22

MicroRNAs

3.1K
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...
3.1K
Protein Networks02:26

Protein Networks

4.1K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.1K
Experimental RNAi02:15

Experimental RNAi

6.2K
RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
6.2K
siRNA - Small Interfering RNAs02:30

siRNA - Small Interfering RNAs

16.9K
Small interfering RNAs, or siRNAs, are short regulatory RNA molecules that can silence genes post-transcriptionally, as well as the transcriptional level in some cases. siRNAs are important for protecting cells against viral infections and silencing transposable genetic elements.
In the cytoplasm, siRNA is processed from a double-stranded RNA, which comes from either endogenous DNA transcription or exogenous sources like a virus. This double-stranded RNA is then cleaved by the...
16.9K

You might also read

Related Articles

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

Sort by
Same author

Fusion of EEMD with extra trees-gradient boosting feature extraction for quantification analysis of polysaccharides and saponins in turnip.

Food chemistry·2026
Same author

SynCraft: an integrated web server for ADMET-aware retrosynthesis and molecular design.

Nucleic acids research·2026
Same author

DeepMIF: A Multiview Interactive Fusion-Based Deep Learning Method for RNA-Small Molecule Binding Affinity Prediction.

Journal of chemical information and modeling·2026
Same author

The regulation characterization of novel transcription regulator JTY_2262 in Mycobacterium bovis BCG.

Biochimie·2026
Same author

HHGSynergy: An Adaptive Heterogeneous Hypergraph Representation Learning Method for Anticancer Drug Synergy Prediction.

IEEE transactions on computational biology and bioinformatics·2025
Same author

HECLCDA:CircRNA-Drug Sensitivity Prediction via Heterogeneous Cross-Scale Contrastive Learning.

IEEE transactions on computational biology and bioinformatics·2025
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
See all related articles

Related Experiment Video

Updated: Aug 25, 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

ISLMI:Predicting lncRNA-miRNA Interactions Based on Information Injection and Second-Order Graph Convolution Network.

Jinmiao Song, Shengwei Tian, Long Yu

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |October 17, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces ISLMI, a novel computational model for predicting long non-coding RNA (lncRNA)-microRNA (miRNA) interactions. ISLMI effectively utilizes network topology and feature representations to enhance prediction accuracy.

    More Related Videos

    Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
    07:24

    Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

    Published on: July 9, 2021

    2.5K
    Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen
    11:32

    Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

    Published on: May 24, 2017

    12.2K

    Related Experiment Videos

    Last Updated: Aug 25, 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
    Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
    07:24

    Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

    Published on: July 9, 2021

    2.5K
    Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen
    11:32

    Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

    Published on: May 24, 2017

    12.2K

    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Long non-coding RNA (lncRNA) and microRNA (miRNA) interactions regulate gene expression and biological activities.
    • Existing methods for identifying lncRNA-miRNA interactions often fail to fully leverage network structural information.

    Purpose of the Study:

    • To develop an advanced computational model, ISLMI, for accurate prediction of lncRNA-miRNA interactions.
    • To improve upon existing methods by incorporating network topology and diverse feature representations.

    Main Methods:

    • ISLMI employs a second-order graph convolution network (SOGCN) combined with information injection.
    • Calculates sequence and Gaussian interaction profile kernel similarity, fuses them, and learns second-order representations.
    • Integrates multiple graph embedding features and utilizes matrix completion to enhance accuracy.

    Main Results:

    • ISLMI demonstrated reliable performance in 5-fold cross-validation.
    • The model significantly improved the accuracy of predicting lncRNA-miRNA interactions compared to existing algorithms.
    • ISLMI's superiority was confirmed through comparative analysis with other models.

    Conclusions:

    • ISLMI effectively integrates sequence, network topology, and feature representations for superior lncRNA-miRNA interaction prediction.
    • The proposed method offers a significant advancement in computational approaches for understanding gene regulatory networks.