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 Experiment Video

Updated: Apr 15, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K

A semi-supervised learning framework for biomedical event extraction based on hidden topics.

Deyu Zhou1, Dayou Zhong1

  • 1School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing, Jiangsu Province 210096, China.

Artificial Intelligence in Medicine
|April 13, 2015
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Mitigating bias in multilabel medical text classification: a cooperative training framework with dynamic debiasing.

Bioinformatics (Oxford, England)·2026
Same author

Correction: Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree.

PloS one·2024
Same author

MIT: Mutual Information Topic Model for Diverse Topic Extraction.

IEEE transactions on neural networks and learning systems·2024
Same author

Does green finance reduce environmental pollution?-a study based on China's provincial panel data.

Environmental science and pollution research international·2023
Same author

Reductive Aza-Pauson-Khand Reaction of Nitriles.

Journal of the American Chemical Society·2023
Same author

Older adults' refusal speech act in cognitive assessment: A multimodal pragmatic perspective.

Frontiers in psychology·2023
Same journal

Real-time EEG-based epileptic seizure prediction using artificial intelligence: A systematic review.

Artificial intelligence in medicine·2026
Same journal

R-peak detection and ECG data compression scheme based on empirical mode decomposition and wavelet transform.

Artificial intelligence in medicine·2026
Same journal

CastNet: A three-channel EEG-based deep learning model for cross-subject depression detection.

Artificial intelligence in medicine·2026
Same journal

State-of-the-art TinyML approaches for colorectal cancer detection: Current advances, challenges, and future directions.

Artificial intelligence in medicine·2026
Same journal

JRadiEvo: A Japanese radiology report generation model enhanced by evolutionary optimization of model merging.

Artificial intelligence in medicine·2026
Same journal

Causally-informed deep learning towards explainable and generalizable outcome prediction in critical care.

Artificial intelligence in medicine·2026
See all related articles

This study introduces a semi-supervised learning framework for biomedical event extraction, improving accuracy by utilizing un-annotated data. The novel approach enhances knowledge discovery from scientific literature, aiding drug reaction and disease development research.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Natural Language Processing

Background:

  • Understanding protein and RNA interactions is crucial for drug discovery and disease research.
  • Life science literature lacks explicit structure, hindering automated knowledge extraction.
  • Biomedical event extraction aims to automatically acquire molecular event knowledge from research articles.

Purpose of the Study:

  • To develop a semi-supervised learning framework for biomedical event extraction.
  • To address the challenge of limited annotated data in the biomedical domain.
  • To improve the accuracy and efficiency of extracting molecular events from text.

Main Methods:

  • A semi-supervised learning framework utilizing hidden topics is proposed.
Keywords:
Biomedical event extractionK nearest neighborLatent Dirichlet allocationSemi-supervised learning

Related Experiment Videos

Last Updated: Apr 15, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K
  • Un-annotated sentences are automatically assigned event annotations based on similarity to annotated data.
  • Sentence structures and hidden topics are used to measure sentence similarity for annotation.

    • Main Results:

    • The framework achieved over 2.2% improvement in F-score on a standard corpus.
    • Experimental results demonstrate the effectiveness of the proposed approach compared to state-of-the-art methods.
    • The method successfully leverages un-annotated data to enhance extraction performance.

    Conclusions:

    • Incorporating un-annotated data significantly improves biomedical event extraction.
    • Hidden topics and sentence structures accurately represent semantic similarity for event extraction.
    • The proposed framework offers a feasible solution for data-scarce biomedical NLP tasks.