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Updated: Oct 6, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways
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Making Biomedical Sciences publications more accessible for machines.

Joris Van Meenen1,2, Hanne Leysen1, Hongyu Chen3

  • 1Receptor Biology Lab, Department of Biomedical Sciences, University of Antwerp, Wilrijk, 2610, Antwerp, Belgium.

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|January 18, 2022
PubMed
Summary
This summary is machine-generated.

To manage the overwhelming volume of biomedical research, integrating machine readability into scientific publications is essential. This human-machine synergy will improve data retrieval and discovery of new insights.

Keywords:
InteroperabilityMachineOpen accessReproducibilityResearch

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Area of Science:

  • Biomedical Sciences
  • Scientific Communication
  • Computational Biology

Background:

  • The exponential growth of biomedical literature presents significant challenges for researchers in accessing and interpreting scientific findings.
  • Databases like PubMed contain millions of citations, with thousands added daily, exacerbating information overload.
  • Current publication formats often hinder machine-based analysis due to poor searchability and inaccessible data presentation.

Purpose of the Study:

  • To advocate for increased synergy between scientific publications and machine-based approaches.
  • To highlight the necessity of adapting manuscript structures for machine-assisted interrogation.
  • To propose that biomedical scientists should author machine-readable manuscripts.

Main Methods:

  • Conceptual framework proposing enhanced human-machine collaboration in scientific literature.
  • Analysis of current limitations in biomedical manuscript accessibility for computational tools.
  • Recommendations for adapting publication formats to facilitate machine learning and data mining.

Main Results:

  • Implementing machine-readable formats can significantly augment the assessment and cataloging of biomedical literature.
  • Enhanced human-machine synergy offers a crucial pathway for researchers to effectively process vast amounts of biomedical data.
  • Adapting manuscript structures will improve the machine-assisted interrogation of the biomedical literary corpus.

Conclusions:

  • A shift towards machine-readable scientific publications is vital for the future of biomedical research.
  • Enhanced collaboration between humans and machines in processing scientific literature will accelerate discovery.
  • Biomedical scientists writing machine-readable manuscripts will unlock novel insights from complex datasets.