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Related Concept Videos

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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Related Experiment Video

Updated: May 9, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Semantator: semantic annotator for converting biomedical text to linked data.

Cui Tao1, Dezhao Song, Deepak Sharma

  • 1School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, United States; Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, United States.

Journal of Biomedical Informatics
|July 23, 2013
PubMed
Summary
This summary is machine-generated.

Semantator transforms unstructured biomedical text into linked data for easier querying. This semantic-web tool aids researchers in annotating, browsing, and inferring knowledge from complex biomedical documents.

Keywords:
Clinical narrativesProtege pluginSemantatorSemantic AnnotationSemantic web

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Last Updated: May 9, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Area of Science:

  • Biomedical Informatics
  • Semantic Web Technologies
  • Data Management

Background:

  • Over 80% of biomedical data exists as unstructured plain text, hindering efficient data access and analysis.
  • Current methods for querying and browsing biomedical text data are often challenging due to its unstructured nature.

Purpose of the Study:

  • To introduce Semantator, a semantic-web-based environment for annotating, browsing, and querying biomedical data.
  • To enable manual and semi-automatic annotation of biomedical documents.
  • To facilitate the use of annotated data in semantic reasoning and knowledge inference.

Main Methods:

  • Development of Semantator, a semantic-web environment.
  • Integration of manual and semi-automatic annotation capabilities using plug-in information extraction tools.
  • Storage of annotated data in RDF format for querying with SPARQL and application of semantic reasoners.

Main Results:

  • Semantator successfully performs its designed annotation functionalities.
  • The tool is suitable for real-world applications in clinical and translational research.
  • Annotated data generated by Semantator is compatible with semantic web-based reasoning tools.

Conclusions:

  • Semantator provides an effective solution for converting unstructured biomedical text into queryable linked data.
  • The tool enhances the accessibility and utility of biomedical information for research and knowledge discovery.
  • Semantator supports advanced data analysis through semantic reasoning and inference capabilities.