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

Neural Circuits01:25

Neural Circuits

2.6K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.6K
Aggregates Classification01:29

Aggregates Classification

970
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
970

You might also read

Related Articles

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

Sort by
Same author

CircPrkcsh, a circular RNA, contributes to the polarization of microglia towards the M1 phenotype induced by spinal cord injury and acts via the JNK/p38 MAPK pathway.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2021
Same author

Air conditioner filters become sinks and sources of indoor microplastics fibers.

Environmental pollution (Barking, Essex : 1987)·2021
Same author

Impacts of salt-tolerant Staphylococcus nepalensis 5-5 on bacterial composition and biogenic amines accumulation in fish sauce fermentation.

International journal of food microbiology·2021
Same author

Molecular identification and phylogenetic analysis of Papaver based on ITS2 barcoding.

Journal of forensic sciences·2021
Same author

A preliminary study of KAT2A on cGAS-related immunity in inflammation amplification of systemic lupus erythematosus.

Cell death & disease·2021
Same author

Improving efficiency of inference in clinical trials with external control data.

Biometrics·2021

Related Experiment Video

Updated: Jan 17, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

A Novel Low-Resource Graph Convolutional Neural Network Approach for Entity Recognition.

Zhen Zhao, Xinyu Li

    IEEE Transactions on Computational Biology and Bioinformatics
    |September 24, 2025
    PubMed
    Summary
    This summary is machine-generated.

    We developed BGBGCN, a novel low-resource graph convolutional neural network for entity recognition. This method achieves results comparable to large models, addressing computational demands in the biomedical domain.

    More Related Videos

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.9K
    Automated Analysis of C. elegans Fluorescence Images using SegElegans
    06:27

    Automated Analysis of C. elegans Fluorescence Images using SegElegans

    Published on: October 10, 2025

    593

    Related Experiment Videos

    Last Updated: Jan 17, 2026

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    1.0K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.9K
    Automated Analysis of C. elegans Fluorescence Images using SegElegans
    06:27

    Automated Analysis of C. elegans Fluorescence Images using SegElegans

    Published on: October 10, 2025

    593

    Area of Science:

    • Natural Language Processing
    • Bioinformatics
    • Machine Learning

    Background:

    • Large language models (LLMs) are advancing entity recognition but require substantial computational resources.
    • Existing models like BERT have limited usability in the biomedical domain due to extensive data needs for pre-training and fine-tuning.
    • There is a need for efficient, low-resource methods for biomedical entity recognition.

    Purpose of the Study:

    • To propose a novel, low-resource graph convolutional neural network (GCN) method for entity recognition, named BGBGCN.
    • To address the high computational and data requirements of current LLMs in the biomedical domain.
    • To improve the usability of entity recognition models in resource-constrained settings.

    Main Methods:

    • Developed BGBGCN by integrating a bidirectional GCN with a bidirectional long short-term memory network.
    • Jointly modeled sentence dependencies and topological structures, considering linear and internal entity features.
    • Introduced a novel feature integration unit (RAFI) using a gating mechanism and graph encoder for interactive feature learning.
    • Utilized convolutional neural networks to enhance character-level embeddings, mitigating out-of-vocabulary word issues.

    Main Results:

    • The proposed BGBGCN model achieved performance comparable to large-scale models like PubMedBERT.
    • Demonstrated the effectiveness of the integrated GCN and LSTM architecture in capturing complex linguistic features.
    • Showcased the utility of the RAFI unit for effective feature fusion.
    • Validated the improvement in handling out-of-vocabulary words through CNN-enhanced embeddings.

    Conclusions:

    • BGBGCN offers a viable low-resource alternative for entity recognition in the biomedical domain.
    • The model effectively balances performance with reduced computational and data demands.
    • This approach enhances the practical applicability of advanced NLP techniques in specialized fields.