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Related Experiment Video

Updated: May 17, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Biomolecular event trigger detection using neighborhood hash features.

Yijia Zhang1, Hongfei Lin, Zhihao Yang

  • 1School of Computer Science, Dalian University of Technology, No.2 Linggong Lu, Dalian 116023, China. Zhyj@dlut.edu.cn

Journal of Theoretical Biology
|November 10, 2012
PubMed
Summary
This summary is machine-generated.

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This study introduces neighborhood hash features for improved biomolecular event detection in proteomics research. This method enhances the extraction of key protein interactions from biomedical literature, achieving state-of-the-art results.

Area of Science:

  • Biochemistry and Proteomics
  • Computational Biology
  • Natural Language Processing

Background:

  • Biomolecular events, especially in proteins, are crucial in proteomics.
  • Biomedical literature is a primary source for extracting these events.
  • Event trigger word detection is the initial step in computational event extraction.

Purpose of the Study:

  • To develop an efficient method for detecting bio-event triggers from biomedical literature.
  • To map sentence dependency graphs into effective semantic/syntactic features for event detection.

Main Methods:

  • Utilized hash operations to iteratively compute dependency graphs.
  • Mapped dependency graphs into neighborhood hash features.
  • Combined neighborhood hash features with basic features for trigger detection.

Related Experiment Videos

Last Updated: May 17, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Main Results:

  • Neighborhood hash features effectively represent semantic/syntactic information from sentence dependency graphs.
  • Neighborhood hash features and basic features are complementary for biomolecular trigger detection.
  • The proposed approach achieved state-of-the-art performance on BioNLP datasets.

Conclusions:

  • Neighborhood hash features offer a robust method for enhancing bio-event trigger detection.
  • This approach improves the computational mining of biomolecular events from scientific text.
  • The findings advance the field of proteomics through better information extraction from literature.