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Related Experiment Video

Updated: Jun 20, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

GEM-TREND: a web tool for gene expression data mining toward relevant network discovery.

Chunlai Feng1, Michihiro Araki, Ryo Kunimoto

  • 1Department of Systems Bioscience for Drug Discovery, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshidashimoadachi-cho, Sakyo-ku, Kyoto 606-8501, Japan.

BMC Genomics
|September 5, 2009
PubMed
Summary
This summary is machine-generated.

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Researchers can now find similar gene expression profiles and build co-expression networks using GEM-TREND, a new web tool. This tool effectively searches the Gene Expression Omnibus (GEO) database for biologically related gene expression entries, aiding in gene function discovery.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • DNA microarray technology enables large-scale gene function discovery.
  • Public databases like Gene Expression Omnibus (GEO) store vast amounts of gene expression data.
  • Manual data retrieval from GEO is time-consuming and often overlooks gene expression patterns.

Purpose of the Study:

  • To develop a user-friendly web tool for effective utilization of public gene expression data.
  • To enable researchers to find similar gene expression data and generate co-expression networks.
  • To facilitate the discovery of gene functions on a genomic scale.

Main Methods:

  • Developed GEM-TREND, a web tool for querying GEO gene expression data.
  • Utilized gene-expression signatures or gene expression ratio data as queries.

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  • Employed a nonparametric, rank-based pattern matching approach with statistical significance calculation.

    • Main Results:

    • GEM-TREND successfully retrieves gene expression entries biologically related to a query from GEO.
    • Validated the tool's efficacy using both random GEO data and in-house microarray data.
    • Integrated a network visualization interface for dynamic linking to external data repositories.

    Conclusions:

    • GEM-TREND facilitates retrieval of similar gene expression profiles from GEO.
    • The tool supports the construction of gene co-expression networks.
    • GEM-TREND is a valuable, user-friendly resource for knowledge discovery in gene expression analysis.