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

Associative Learning01:27

Associative Learning

982
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
982
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

407
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
407
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

446
In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
446
Types Of Transformers01:16

Types Of Transformers

1.3K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.3K
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

344
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
344
Synthetic Biology02:55

Synthetic Biology

5.3K
Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
5.3K

You might also read

Related Articles

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

Sort by
Same author

Multi-task deep learning for sub-clinical screening of mood and sleep disturbances using physical activity biomarkers.

Journal of affective disorders·2026
Same author

An exploratory study of multi-channel CNN for early detection of lung cancer from longitudinal healthcare records.

Scientific reports·2026
Same author

Comparative Analysis of Large Language Models and Machine Learning for ASA Classification Using Structured Electronic Health Record Data.

Journal of medical systems·2026
Same author

PoseShot: hybrid CNN-BiLSTM transformer model for free throw action recognition via pose analysis.

Scientific reports·2026
Same author

Longitudinal multisource clinical model for early lung cancer risk stratification and screening.

BMJ health & care informatics·2026
Same author

Early detection of female-specific cancers using longitudinal healthcare records with a multichannel convolutional neural network.

BMJ health & care informatics·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Dec 11, 2025

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

421

LBERT: Lexically aware Transformer-based Bidirectional Encoder Representation model for learning universal bio-entity

Neha Warikoo1,2,3, Yung-Chun Chang4,5,6, Wen-Lian Hsu3,6

  • 1Institute of Biomedical Informatics, National Yang-Ming University, Taipei 112, Taiwan.

Bioinformatics (Oxford, England)
|August 19, 2020
PubMed
Summary
This summary is machine-generated.

We introduce LBERT, a novel Lexically aware Transformer-based Bidirectional Encoder Representation model for Bio-Entity Relation Extraction. LBERT improves upon existing methods by integrating local and global contexts, enhancing biomedical knowledge discovery.

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.5K
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

396

Related Experiment Videos

Last Updated: Dec 11, 2025

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

421
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.5K
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

396

Area of Science:

  • Biomedical Natural Language Processing
  • Bioinformatics
  • Computational Biology

Background:

  • Advanced Natural Language Processing (NLP) is crucial for managing and structuring the growing volume of biomedical data.
  • Bio-Entity Relation Extraction (BRE) is vital for knowledge discovery in the biomedical domain.
  • Current deep learning models for BRE often lack task universality and fail to incorporate local syntactic context.

Purpose of the Study:

  • To propose a universal Bio-Entity Relation Extraction (BRE) model, LBERT (Lexically aware Transformer-based Bidirectional Encoder Representation), that captures both local and global contextual information.
  • To evaluate LBERT's performance across various biomedical relation types, including protein-protein interaction (PPI), drug-drug interaction, and protein-bio-entity relations.
  • To demonstrate the effectiveness of lexical features and distance-adjusted attention in enhancing BRE predictive accuracy.

Main Methods:

  • Development of LBERT, a novel Lexically aware Transformer-based Bidirectional Encoder Representation model.
  • Exploration of both local syntactic and global semantic contexts for sentence-level classification tasks.
  • Comparative analysis against state-of-the-art deep learning models on multiple BRE tasks.

Main Results:

  • LBERT significantly outperforms existing deep learning models in protein-protein interaction (PPI), drug-drug interaction, and protein-bio-entity relation classification.
  • LBERT demonstrates statistically significant improvements over BioBERT in detecting bio-entity relations within large corpora like PPI.
  • Ablation studies confirm the contribution of lexical features and distance-adjusted attention to improved prediction performance.

Conclusions:

  • LBERT offers a universal and effective approach to Bio-Entity Relation Extraction by integrating lexical awareness and multi-contextual representations.
  • The model's superior performance highlights the importance of incorporating local syntactic information alongside global context in biomedical NLP.
  • LBERT represents a significant advancement in structuring biomedical knowledge and facilitating data-driven discoveries.