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Related Experiment Video

Updated: Jun 29, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms
10:41

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Published on: May 9, 2017

Automating curation using a natural language processing pipeline.

Beatrice Alex1, Claire Grover, Barry Haddow

  • 1School of Informatics, University of Edinburgh, Edinburgh, UK. balex@inf.ed.ac.uk

Genome Biology
|October 18, 2008
PubMed
Summary
This summary is machine-generated.

This study adapted natural language processing (NLP) for biomedical literature curation, achieving high performance on interaction tasks and competitive results for gene mention recognition. NLP systems show promise but still face challenges in complex tasks like protein pair normalization.

Related Experiment Videos

Last Updated: Jun 29, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms
10:41

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Published on: May 9, 2017

Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Natural Language Processing

Background:

  • BioCreative II tasks simulate laborious biomedical literature curation.
  • The University of Edinburgh team adapted an existing natural language processing (NLP) system for these tasks.
  • The NLP system serves as a commercial curation assistant and can extract information relevant to biologists.

Purpose of the Study:

  • To evaluate the adaptability and performance of an NLP system on BioCreative II curation tasks.
  • To assess the system's effectiveness in assisting with biomedical research paper curation.
  • To explore the general applicability of the NLP system for extracting biological information.

Main Methods:

  • Adaptation and extension of a pre-existing natural language processing (NLP) system.
  • Application of the NLP system to specific BioCreative II subtasks, including gene mention and normalization.
  • Utilizing string matching techniques for rapid domain adaptation in gene normalization.

Main Results:

  • The system achieved high performance on interaction subtasks.
  • Competitive performance was obtained for the gene mention task with minimal development effort.
  • String matching for gene normalization performed close to average and is quickly applicable to new domains.

Conclusions:

  • The developed NLP technologies are readily adaptable to BioCreative II tasks.
  • High performance is achievable on individual tasks like gene mention recognition and normalization.
  • Complex, multi-component tasks, such as detecting and normalizing interacting protein pairs, remain challenging for current NLP systems.