Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Which gene did you mean?

Barend Mons1

  • 1Biosemantics Group Rotterdam, Department of Medical Informatics, Erasmus MC - University Medical Center Rotterdam, P,O, Box 1738, NL-3000 DR Rotterdam, the Netherlands. b.mons@erasmusmc.nl

BMC Bioinformatics
|June 9, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The FAIR data point populator: collaborative FAIRification and population of FAIR data points.

BMC medical informatics and decision making·2025
Same author

Operationalizing and digitizing person-centered daily functioning: a case for functionomics.

BMC medical informatics and decision making·2024
Same author

The use of foundational ontologies in biomedical research.

Journal of biomedical semantics·2023
Same author

Playing catch-up in building an open research commons.

Science (New York, N.Y.)·2022
Same author

FAIR Digital Twins for Data-Intensive Research.

Frontiers in big data·2022
Same author

Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic.

Journal of biomedical semantics·2022
Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
See all related articles

Semantic tagging of electronic publishing is crucial for computational biology. Integrating this process enhances computer-readable data analysis for large datasets from high-throughput experiments.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Scientific Publishing

Background:

  • Computational Biology requires computer-readable data for analyzing large datasets from high-throughput experiments.
  • Current text-mining approaches for information extraction face challenges with ambiguity and have not met expectations for widespread adoption.
  • Semantic enrichment of plain text is vital for advancing computer-aided biological analysis.

Discussion:

  • Semantic tagging, when integrated into electronic publishing, offers a proactive solution to data ambiguity.
  • This approach contrasts with traditional text-mining methods that retrospectively extract information.
  • The negative perception of text mining among biologists stems from past disappointments with classical tools.

Key Insights:

Related Experiment Videos

  • Proposes integrating semantic tagging directly into the electronic publishing workflow.
  • Highlights the need for computer-readable information records in Computational Biology.
  • Emphasizes semantic enrichment for effective analysis of massive biological datasets.

    • Outlook:

    • Semantic tagging as an integral part of electronic publishing can overcome limitations of retrospective text mining.
    • This integration aims to improve the usability and reliability of biological data for computational analysis.
    • Facilitates more robust and efficient follow-up of high-throughput experiments and other data-intensive investigations.