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StemTextSearch: Stem cell gene database with evidence from abstracts.

Chou-Cheng Chen1, Chung-Liang Ho2

  • 1Department of Institute of Basic Medical Sciences, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan.

Journal of Biomedical Informatics
|March 19, 2017
PubMed
Summary
This summary is machine-generated.

StemTextSearch is a novel text-mining tool that identifies gene roles in stem cells. It analyzes gene regulation, stem cell regulation, and cellular processes within biomedical abstracts to determine positive or negative gene functions.

Keywords:
Gene roleStem cellText mining

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Area of Science:

  • Biomedical informatics
  • Stem cell biology
  • Bioinformatics

Background:

  • Previous research utilized diverse methods like text mining and experimental data to identify stem cell biomarkers.
  • A gap existed in text-mining approaches for determining a gene's specific positive or negative role in stem cells.

Purpose of the Study:

  • To develop a text-mining method for identifying the specific role (positive or negative) of genes in stem cells.
  • To create a searchable engine for exploring gene functions within the context of stem cell biology.

Main Methods:

  • Developed StemTextSearch, a text-mining engine.
  • Analyzed biomedical abstracts for co-occurrences of gene regulation, stem cell regulation, and cell process terms within the same sentences.

Main Results:

  • The StemTextSearch dataset encompasses 5797 genes, categorizing their roles as positive (1534), negative (1335), dual (1654), or uncertain (1274).
  • Achieved a precision of 0.66 and a recall of 0.78 for identifying gene roles.
  • The engine allows queries based on gene, stem cell category, gene role, gene regulation, cell process, stem cell regulation, and species.

Conclusions:

  • StemTextSearch successfully identifies the role of genes in stem cells using a text-mining approach.
  • The tool provides a valuable resource for stem cell research by elucidating gene functions.