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Related Experiment Video

Updated: May 29, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Biomedical events extraction using the hidden vector state model.

Deyu Zhou1, Yulan He

  • 1School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu Province, China. d.zhou@seu.edu.cn

Artificial Intelligence in Medicine
|September 28, 2011
PubMed
Summary
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This study introduces HVS-BioEvent, a novel system for extracting complex biomedical events from text. The hidden vector state (HVS) model effectively captures intricate relationships, outperforming existing methods for specific event types.

Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Computational Biology

Background:

  • Biomedical event extraction aims to identify molecular state changes described in literature.
  • Extracting complex biomedical events, involving hierarchical relations, is more challenging than simpler protein-protein interaction extraction.

Purpose of the Study:

  • To propose and evaluate HVS-BioEvent, an information extraction system utilizing the hidden vector state (HVS) model.
  • To investigate the system's capability in extracting complex biomedical events.

Main Methods:

  • Developed HVS-BioEvent, an information extraction system based on the hidden vector state (HVS) model.
  • Implemented automated abstract annotation generation for HVS training.
  • Introduced novel machine learning approaches for event trigger identification and extraction.

Related Experiment Videos

Last Updated: May 29, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Main Results:

  • Achieved an F-score of 49.57% on the BioNLP'09 shared task corpus.
  • Outperformed the top-performing system (UTurku) in extracting complex regulation events (36.57% vs. 30.52%) and negative regulation events (40.61% vs. 38.99%).

Conclusions:

  • The hidden vector state (HVS) model, with its hierarchical structure, is well-suited for complex event extraction.
  • The proposed system demonstrates the effectiveness of HVS in modeling embedded structural context for biomedical event extraction.