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

A text-mining perspective on the requirements for electronically annotated abstracts.

Florian Leitner1, Alfonso Valencia

  • 1Structural Computational Biology Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.

FEBS Letters
|March 11, 2008
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

Intelligent tool orchestration for rapid mechanistic model prototyping: MCP servers as AI-biology interfaces.

NPJ systems biology and applications·2026
Same author

IMPaCT-Data: A Federated Precision Medicine Infrastructure Associated with Science and Technology in Spain.

Studies in health technology and informatics·2026
Same author

Commonalities in frailty and psychopathology predict chronotype across severe mental disorders from a comorbidity perspective.

Psychological medicine·2026
Same author

Leveraging training expertise to build capacity in computational personalised medicine.

Bioinformatics advances·2026
Same author

Deep representation learning for temporal inference in cancer omics: a systematic literature review.

Briefings in bioinformatics·2026
Same author

Airway organoids reveal patterns of influenza A tropism and adaptation in wildlife species.

Emerging microbes & infections·2026
Same journal

Extending the classical sequence-structure-function paradigm through protein dynamics and context-dependent behavior.

FEBS letters·2026
Same journal

α-Synuclein aggregation landscape from phase separation to neurotoxic intermediates.

FEBS letters·2026
Same journal

Modelling stem cell differentiation related processes-A practical overview for biologists.

FEBS letters·2026
Same journal

Overlapping gut microbiome signatures in aging and disease are characterized by enrichment of medication-associated oral microbes in the gut.

FEBS letters·2026
Same journal

Csk binding to integrin β3 is regulated by tyrosine and threonine phosphorylation of β3.

FEBS letters·2026
Same journal

Mixed-class J-domain protein scaffolds promote expanded aggregate handling and multivalent Hsp70 engagement during functional disaggregase assembly.

FEBS letters·2026
See all related articles

We propose combining human expertise with text-mining to create electronically annotated information (EAI) for scientific abstracts. This approach enhances data categorization, database curation, and the link between databases and published research.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Text Mining

Background:

  • Scientific literature contains vast amounts of valuable biological data.
  • Manual curation of this data for databases is time-consuming and labor-intensive.
  • Existing text-mining systems have limitations in accuracy and scope.

Purpose of the Study:

  • To propose a hybrid approach combining human expertise and automated text mining for generating electronically annotated information (EAI).
  • To focus initial efforts on annotating gene/protein and organism names.
  • To lay the groundwork for future systems capable of annotating more complex biological information like interactions and functions.

Main Methods:

  • Integration of human expertise with automatic text-mining systems.

Related Experiment Videos

  • Development of a first generation of electronically annotated information (EAI).
  • Initial experiments focused on annotating gene/protein and organism names.

    • Main Results:

    • Demonstrated the feasibility of creating EAI by combining human and machine annotation.
    • Successfully annotated gene/protein and organism names, addressing well-resolved problems.
    • Established a foundation for future advancements in annotating complex biological data.

    Conclusions:

    • The proposed hybrid approach offers a viable method for generating EAI to enrich scientific abstracts.
    • EAI facilitates easier information categorization, aids in database curation, and strengthens the link between databases and literature.
    • This work contributes to improving database completeness and developing advanced text-mining training datasets.