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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

Text mining.

Andrew B Clegg1, Adrian J Shepherd

  • 1Institute of Structural Molecular Biology, School of Crystallography, Birkbeck College, University of London, London, United Kingdom.

Methods in Molecular Biology (Clifton, N.J.)
|August 21, 2008
PubMed
Summary
This summary is machine-generated.

Bioinformatics text mining uses natural language processing (NLP) to extract knowledge from biomedical literature. A new tool identifies and maps gene/protein names, a crucial step for bioinformatics applications.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Biomedical Informatics

Background:

  • Text mining is rapidly advancing in bioinformatics, utilizing natural language processing (NLP) for knowledge discovery.
  • Biomedical text analysis involves techniques from simple proximity analysis to complex parsing for relationship extraction.
  • Key challenges include managing and extracting information from large-scale biological and biomedical text corpora like MEDLINE.

Purpose of the Study:

  • To introduce fundamental principles and challenges in natural language processing for bioinformatics.
  • To present available tools for end-users and developers in biomedical text mining.
  • To demonstrate a practical application through a case study of gene/protein name identification.

Main Methods:

  • Overview of natural language processing (NLP) techniques applicable to biomedical text.
  • Introduction to software tools and resources for text mining.
  • Development and evaluation of a specific tool for gene/protein name recognition and database mapping.

Main Results:

  • The chapter provides a foundational understanding of NLP in bioinformatics.
  • Various NLP tools and their applications in knowledge discovery are discussed.
  • A functional case study tool for gene/protein name identification was successfully constructed and tested.

Conclusions:

  • Text mining, powered by NLP, is essential for knowledge management in bioinformatics.
  • The development of specialized tools, like the one presented, is critical for advancing bioinformatics research.
  • Effective gene/protein name identification is a foundational task for numerous bioinformatics text mining applications.