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

Bio-medical entity extraction using support vector machines.

Koichi Takeuchi1, Nigel Collier

  • 1Okayama University, 3-1-1 Tsushima-naka, Okayama-shi, Okayama 700-8530, Japan. koichi@cl.it.okayama-u.ac.jp

Artificial Intelligence in Medicine
|April 7, 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

Development of a generative AI agent for family support in implementing family-based treatment for children and adolescents with anorexia nervosa.

Frontiers in digital health·2026
Same author

Utility of Same-Modality, Cross-Domain Transfer Learning for Malignant Bone Tumor Detection on Radiographs: A Multi-Faceted Performance Comparison with a Scratch-Trained Model.

Cancers·2025
Same author

Comparative analysis of a standard (GPT-4o) and reasoning-enhanced (o1 pro) large language model on complex clinical questions from the Japanese orthopaedic board examination.

Journal of orthopaedic science : official journal of the Japanese Orthopaedic Association·2025
Same author

Generative language models exhibit social identity biases.

Nature computational science·2024
Same author

Semantic Speech Networks Linked to Formal Thought Disorder in Early Psychosis.

Schizophrenia bulletin·2023
Same author

A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.

NPJ digital medicine·2022
Same journal

Real-time EEG-based epileptic seizure prediction using artificial intelligence: A systematic review.

Artificial intelligence in medicine·2026
Same journal

R-peak detection and ECG data compression scheme based on empirical mode decomposition and wavelet transform.

Artificial intelligence in medicine·2026
Same journal

CastNet: A three-channel EEG-based deep learning model for cross-subject depression detection.

Artificial intelligence in medicine·2026
Same journal

State-of-the-art TinyML approaches for colorectal cancer detection: Current advances, challenges, and future directions.

Artificial intelligence in medicine·2026
Same journal

JRadiEvo: A Japanese radiology report generation model enhanced by evolutionary optimization of model merging.

Artificial intelligence in medicine·2026
Same journal

Causally-informed deep learning towards explainable and generalizable outcome prediction in critical care.

Artificial intelligence in medicine·2026
See all related articles

Support Vector Machines (SVMs) effectively identify and annotate scientific terms in molecular biology. Combining surface words, orthographic, and head noun features optimizes performance for this specialized domain.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Support Vector Machines (SVMs) excel in classification tasks.
  • Scientific and technical terminology requires specialized identification methods.
  • Extending traditional named entity recognition to domains like molecular biology is crucial.

Purpose of the Study:

  • To apply SVMs for identifying and semantically annotating molecular biology terminology.
  • To demonstrate the extensibility of named entity recognition to specialized, large-scale terminologies.
  • To explore SVM performance in processing scientific text.

Main Methods:

  • Utilizing a domain expert-annotated text sample based on an ontology.
  • Training an SVM model to annotate terms in new texts and contexts.

Related Experiment Videos

  • Applying the model to 100 MEDLINE abstracts from the human, blood cell, and transcription factor domain.

    • Main Results:

    • Approximately 3400 terms were annotated.
    • The SVM model achieved a 74% F-score on cross-validation tests.
    • Empirical analysis identified key feature sets contributing to performance.

    Conclusions:

    • A relationship exists between feature window size and training data amount.
    • A combination of surface words, orthographic features, and head noun features yielded optimal performance.
    • SVMs offer a viable approach for semantic annotation in specialized scientific domains.