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

Classification of Systems-I01:26

Classification of Systems-I

285
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
285
Classification of Systems-II01:31

Classification of Systems-II

219
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
219
Classification of Signals01:30

Classification of Signals

773
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
773
Aggregates Classification01:29

Aggregates Classification

366
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...
366
Stereotype Content Model02:16

Stereotype Content Model

14.8K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.8K
Types Of Transformers01:16

Types Of Transformers

1.0K
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...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Whole Blood Viscosity and Its Associations with Age, Hematologic Indices, and Serum Biochemical Variables in Clinically Healthy Beagle Dogs and Korean Shorthair Cats.

Veterinary sciences·2026
Same author

Pharmacovigilance of Herb-Drug Interactions: A Pharmacokinetic Study on the Combined Administration of Tripterygium Glycosides Tablets and Leflunomide Tablets in Rats by LC-MS/MS.

Pharmaceuticals (Basel, Switzerland)·2022
Same author

Clinical effect of successful reperfusion in patients presenting with NIHSS < 6 and large vessel occlusion.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association·2022
Same author

Safety of inactivated SARS-CoV-2 vaccines in myasthenia gravis: A survey-based study.

Frontiers in immunology·2022
Same author

Arabidopsis plants engineered for high root sugar secretion enhance the diversity of soil microorganisms.

Biotechnology journal·2022
Same author

Simultaneous determination of nirmatrelvir and ritonavir in human plasma using LC-MS/MS and its pharmacokinetic application in healthy Chinese volunteers.

Biomedical chromatography : BMC·2022
Same journal

Classification of periapical radiographic findings for root canal therapy decision support using deep neural networks.

BMC medical informatics and decision making·2026
Same journal

Machine learning-based risk assessment of neonatal perinatal adverse outcomes of anemia during pregnancy: a modeling study.

BMC medical informatics and decision making·2026
Same journal

Intelligent differentiation between Parkinson's disease and essential tremor using wearable sensors and machine learning: a temporal validation study.

BMC medical informatics and decision making·2026
Same journal

Risk prediction of sepsis-associated acute kidney injury: development, validation of a machine learning model with multicenter data.

BMC medical informatics and decision making·2026
Same journal

Trajectory analysis of sleep disorders and anxiety-depression in female breast cancer patients undergoing chemotherapy: based on group-based Multi-Trajectory Model and machine learning.

BMC medical informatics and decision making·2026
Same journal

Multitask learning of longitudinal circulating biomarkers and clinical outcomes: identification of optimal machine-learning and deep-learning models.

BMC medical informatics and decision making·2026
See all related articles

Related Experiment Video

Updated: Aug 29, 2025

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.1K

BertSRC: transformer-based semantic relation classification.

Yeawon Lee1, Jinseok Son2, Min Song3

  • 1Department of Library and Information Science, Yonsei University, Seoul, South Korea.

BMC Medical Informatics and Decision Making
|September 6, 2022
PubMed
Summary
This summary is machine-generated.

Researchers developed a new dataset and a Bidirectional Encoder Representations from Transformers (BERT) model for biomedical relation classification. This approach enhances the extraction of semantic relationships, aiding drug discovery and text mining.

Keywords:
Annotation methodBERTCorpus constructionDeep learningFine-tuningRelation extractionSemantic relation classification

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.9K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

485

Related Experiment Videos

Last Updated: Aug 29, 2025

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.1K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.9K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

485

Area of Science:

  • Biomedical text mining
  • Computational biology
  • Natural Language Processing

Background:

  • Identifying relationships between biomedical entities is crucial for research areas like drug discovery.
  • Manual literature surveys are becoming increasingly difficult due to the exponential growth of biomedical publications.
  • Automated relation classification is vital for efficiently mining meaningful relationships from scientific literature.

Purpose of the Study:

  • To construct a high-quality, expert-annotated dataset for biomedical relation classification.
  • To develop and validate a Bidirectional Encoder Representations from Transformers (BERT) based relation classification model.
  • To introduce a novel fine-tuning methodology to improve relation extraction performance.

Main Methods:

  • Creation of a manually annotated dataset by biomedical experts, focusing on semantic relations between biomedical entities.
  • Development of a relation classification model utilizing Bidirectional Encoder Representations from Transformers (BERT).
  • Application of a newly proposed fine-tuning methodology to the BERT model for enhanced performance.

Main Results:

  • The constructed dataset serves as a valuable resource for developing and evaluating relation extraction models.
  • The BERT model, trained with the proposed fine-tuning methodology, demonstrated superior performance compared to other deep learning models.
  • The fine-tuning methodology significantly improved relation extraction performance.

Conclusions:

  • The developed dataset is essential for advancing biomedical text mining and relation extraction.
  • The proposed fine-tuning methodology offers a promising approach to enhance the accuracy of relation classification models.
  • This work facilitates the discovery of unknown biomedical relationships and supports future text mining research.