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Updated: Jun 19, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
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Graph ranking for exploratory gene data analysis.

Cuilan Gao1, Xin Dang, Yixin Chen

  • 1Department of Mathematics, The University of Mississippi, University, MS 38677, USA.

BMC Bioinformatics
|October 9, 2009
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel gene ranking framework that integrates Gene Ontology (GO) and gene expression data. The method uses a weighted bipartite graph and kernelized spatial depth (KSD) for improved biological insight.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray technology enables high-throughput gene expression analysis.
  • Analyzing vast gene expression datasets presents significant challenges in data interpretation.
  • Existing gene selection methods often lack sufficient biological context.

Purpose of the Study:

  • To develop a robust gene ranking framework integrating biological knowledge with gene expression data.
  • To address the limitations of statistical significance in identifying biologically relevant genes.
  • To provide a method for simultaneous ranking of genes and molecular functions.

Main Methods:

  • Constructed a bipartite graph linking genes and Gene Ontology (GO) molecular functions.
  • Weighted graph edges using gene expression levels under specific species conditions.

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  • Developed a novel ranking algorithm utilizing kernelized spatial depth (KSD) on the weighted graph.

    • Main Results:

    • The proposed framework effectively integrates species-independent and species-dependent biological information.
    • Kernelized spatial depth (KSD) provides a real-valued measure for ranking gene and molecular function importance.
    • The method allows for separate ranking of over-expressed and under-regulated genes.

    Conclusions:

    • The gene-function bigraph successfully integrates GO annotations with gene expression data.
    • The graph representation captures gene relevance through shared molecular functions.
    • The KSD-based approach offers an exploratory framework for analyzing complex gene expression datasets.