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Related Experiment Video

Updated: Dec 15, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Using the PubAnnotation ecosystem to perform agile text mining on Genomics & Informatics: a tutorial review.

Hee-Jo Nam¹, Ryota Yamada², Hyun-Seok Park^1,3

¹Bioinformatics Laboratory, ELTEC College of Engineering, Ewha Womans University, Seoul 03760, Korea.

Genomics & Informatics

|July 8, 2020

Summary

This study introduces a tutorial for named entity recognition using the PubAnnotation ecosystem, leveraging full-text articles from Genomics & Informatics. It details converting and annotating publications for enhanced bioinformatics data discovery.

Keywords:

named entity recognition natural language processing text mining

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Area of Science:

Bioinformatics
Computational Biology
Text Mining

Background:

Genomics & Informatics full-text articles (Volumes 10-17) are available as XML files from PubMed Central (PMC).
A prototype corpus is archived on GitHub.
The Biomedical Linked Annotation Hackathon 6 (BLAH6) explored annotation methods for these articles.

Purpose of the Study:

To provide a tutorial on performing iterative named entity recognition (NER).
To demonstrate the use of the PubAnnotation/PubDictionaries/TextAE ecosystem for text annotation.
To describe a new conversion tool developed during BLAH6.

Main Methods:

Utilized PubAnnotation to process 301 PMC full-text articles from Genomics & Informatics.
Experimented with converting and annotating these articles.
Developed a conversion tool to translate Genia tagger output to PubAnnotation's JSON format.

Main Results:

Successfully demonstrated the iterative NER process within the PubAnnotation ecosystem.
Created a functional conversion tool bridging Genia tagger and PubAnnotation formats.
Facilitated the annotation and updating of a substantial corpus of biomedical literature.

Conclusions:

The PubAnnotation ecosystem offers a practical framework for iterative named entity recognition in scientific literature.
The developed conversion tool enhances interoperability between different bioinformatics annotation pipelines.
This work supports the advancement of structured data extraction from biomedical publications.