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 Video

Updated: Jun 22, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Feature selection techniques for maximum entropy based biomedical named entity recognition.

Sujan Kumar Saha1, Sudeshna Sarkar, Pabitra Mitra

  • 1Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, West Bengal 721 302, India. sujan.kr.saha@gmail.com

Journal of Biomedical Informatics
|June 19, 2009
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 and efficacy testing of an artificial intelligence enabled treatment package (eDOSTHI) for tobacco cessation: study protocol for a randomized controlled trial.

BMC psychiatry·2026
Same author

Self Supervised Prediction of Genetic Associations in Comorbid Diseases With Masked Autoencoder Using Hypergraph Representations.

IEEE transactions on computational biology and bioinformatics·2025
Same author

Design, Synthesis, and Characterization of Polyoxotungstate-Decorated Ionic Liquid-Based Hybrid Material, [BmIm]<sub>4</sub>[SiW<sub>12</sub>O<sub>40</sub>] toward Rapid Adsorption of Dye and Antibacterial Activities.

Inorganic chemistry·2024
Same author

Molecular histopathology of matrix proteins through autofluorescence super-resolution microscopy.

Scientific reports·2024
Same author

Detection of Neurogenic Voice Disorders Using the Fisher Vector Representation of Cepstral Features.

Journal of voice : official journal of the Voice Foundation·2022
Same author

Identification and epidemiological characterization of Type-2 diabetes sub-population using an unsupervised machine learning approach.

Nutrition & diabetes·2022
Same journal

Evaluation of temporal preservation in synthetic longitudinal patient data.

Journal of biomedical informatics·2026
Same journal

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026
Same journal

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Journal of biomedical informatics·2026
Same journal

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation extraction.

Journal of biomedical informatics·2026
Same journal

Beyond Accuracy: Safety-Centered guidelines for the evaluation of LLM-based therapy recommendation systems for chronic multimorbidity patients.

Journal of biomedical informatics·2026
Same journal

DeepEN: A deep reinforcement learning framework for personalized enteral nutrition in critical care.

Journal of biomedical informatics·2026
See all related articles

This study introduces automated feature reduction for biomedical named entity recognition (NER). The approach enhances maximum entropy classifiers, outperforming systems without domain knowledge.

Area of Science:

  • Biomedical text mining
  • Natural Language Processing
  • Bioinformatics

Background:

  • Named entity recognition (NER) is crucial for biomedical text mining, identifying entities like genes and proteins.
  • Recognizing complex biomedical entities is challenging.
  • Machine learning methods (CRF, MEMM, SVM) are common but rely heavily on feature selection.

Purpose of the Study:

  • To investigate word clustering and selection for feature reduction in biomedical NER.
  • To apply these methods using a maximum entropy classifier.
  • To evaluate performance without relying on domain-specific knowledge.

Main Methods:

  • Utilized word clustering for feature generation.
  • Implemented feature selection techniques automatically.

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Related Experiment Videos

Last Updated: Jun 22, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

  • Employed a maximum entropy classifier for named entity recognition.

    • Main Results:

    • The developed system demonstrated superior performance compared to existing methods.
    • Automated feature identification and selection proved effective.
    • The system achieved high accuracy without manual domain knowledge input.

    Conclusions:

    • Automated feature reduction techniques, particularly word clustering, significantly improve biomedical NER.
    • Maximum entropy classifiers combined with optimized features offer a robust approach.
    • This method provides a valuable, domain-knowledge-free solution for biomedical entity extraction.