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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

Biomarker identification using text mining.

Hui Li1, Chunmei Liu

  • 1Department of Systems and Computer Science, Howard University, Washington, DC 20059, USA. hli3302@gmail.com

Computational and Mathematical Methods in Medicine
|December 1, 2012
PubMed
Summary
This summary is machine-generated.

Scientists developed a text-mining method to find molecular biomarkers in PubMed. This approach uses a dictionary-based database and a finite state machine for reliable biomarker discovery from large biological datasets.

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Area of Science:

  • Biomedical Informatics
  • Genomics
  • Molecular Biology

Background:

  • Identifying molecular biomarkers is crucial for understanding disease genotypes and cellular phenotypes.
  • Large-scale biological data analysis requires efficient methods for biomarker discovery.

Purpose of the Study:

  • To propose a novel text-mining-based method for discovering molecular biomarkers.
  • To leverage the PubMed database for identifying potential biomarkers.

Main Methods:

  • Construction of a dictionary-based database.
  • Application of a finite state machine for biomarker identification.
  • Utilizing text mining techniques on scientific literature.

Main Results:

  • The proposed method successfully identifies molecular biomarkers from the PubMed database.
  • The text-mining approach offers a reliable means for biomarker discovery.

Conclusions:

  • Text mining provides a powerful and reliable strategy for discovering molecular biomarkers.
  • This method enhances the assessment of disease-related genotypes and cellular phenotypes.