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

Improving Translational Accuracy02:07

Improving Translational Accuracy

11.6K
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...
11.6K
Aggregates Classification01:29

Aggregates Classification

345
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...
345
Classification of Systems-I01:26

Classification of Systems-I

213
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:
213
Classification of Systems-II01:31

Classification of Systems-II

175
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,
175
Language Development01:22

Language Development

395
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
395
Lateralization01:28

Lateralization

363
Brain lateralization refers to the division of mental processes and functions between the two hemispheres of the brain, a phenomenon that optimizes neural efficiency and underpins complex abilities in humans. This specialization allows each hemisphere to perform tasks where it has a comparative advantage, facilitating more refined cognitive capabilities across different domains.
363

You might also read

Related Articles

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

Sort by
Same author

Evaluating ChatGPT's Adherence to Medical Ethics: A Prerequisite for Artificial Intelligence in Medicine.

Health care science·2026
Same author

Correction: A Multilabel Text Classifier of Cancer Literature at the Publication Level: Methods Study of Medical Text Classification.

JMIR medical informatics·2024
Same author

Potential Schizophrenia Disease-Related Genes Prediction Using Metagraph Representations Based on a Protein-Protein Interaction Keyword Network: Framework Development and Validation.

JMIR formative research·2023
Same author

A Multilabel Text Classifier of Cancer Literature at the Publication Level: Methods Study of Medical Text Classification.

JMIR medical informatics·2023
Same author

Chinese Clinical Named Entity Recognition From Electronic Medical Records Based on Multisemantic Features by Using Robustly Optimized Bidirectional Encoder Representation From Transformers Pretraining Approach Whole Word Masking and Convolutional Neural Networks: Model Development and Validation.

JMIR medical informatics·2023
Same author

Ontological Organization and Bioinformatic Analysis of Adverse Drug Reactions From Package Inserts: Development and Usability Study.

Journal of medical Internet research·2020

Related Experiment Video

Updated: Jul 19, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

619

A Deep Learning Model for the Normalization of Institution Names by Multisource Literature Feature Fusion: Algorithm

Yifei Chen1, Xiaoying Li1, Aihua Li1

  • 1Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China.

JMIR Formative Research
|August 18, 2023
PubMed
Summary

A new deep learning model accurately normalizes institution names, improving literature retrieval and research analysis. This system identifies variants, updates databases, and achieves 93.79% accuracy for better institutional evaluation.

Keywords:
BERTbidirectional encoder representations from transformersdeep learninginstitution name normalizationmultisource literature

More Related Videos

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.5K
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

570

Related Experiment Videos

Last Updated: Jul 19, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

619
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.5K
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

570

Area of Science:

  • Bibliometrics and scientometrics
  • Information science
  • Artificial intelligence in research

Background:

  • Institution name variations hinder accurate literature retrieval and analysis of academic achievements.
  • Standardizing institutional names is crucial for evaluating research competitiveness.
  • Deep learning offers advanced natural language processing for improved name normalization.

Purpose of the Study:

  • To develop a deep learning model for institution name normalization using fused affiliation data.
  • To achieve high accuracy in normalizing diverse institution name variants via authority files.
  • To enhance the reliability of publication data analysis.

Main Methods:

  • Utilized Bidirectional Encoder Representations from Transformers (BERT) and other deep learning models.
  • Incorporated institution classification, hierarchical relation extraction, and matching/merging models.
  • Trained the model on pretraining and fine-tuning using data from Dimensions, Web of Science, and Scopus.

Main Results:

  • The model accurately identifies and links standard institution names to unique IDs.
  • It detects and updates nonstandard variants (e.g., abbreviations, plurals) in authority files.
  • Achieved a 93.79% accuracy rate in institution name normalization, including handling unregistered institutions.

Conclusions:

  • The developed deep learning model demonstrates high accuracy for institution name normalization.
  • This tool has significant potential for evaluating institutional competitiveness and research impact.
  • Applications include analyzing institutional research fields and building cooperation networks.