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Related Experiment Video

Updated: Jun 21, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Semantic similarity based feature extraction from microarray expression data.

Young-Rae Cho1, Aidong Zhang, Xian Xu

  • 1Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA. ycho8@cse.buffalo.edu

International Journal of Data Mining and Bioinformatics
|July 24, 2009
PubMed
Summary
This summary is machine-generated.

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This study introduces a new method using gene expression and Gene Ontology to create accurate sample classifiers. The approach enhances colon cancer classification accuracy by over 10% through novel feature extraction.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Classifiers are feasible using gene expression profiles.
  • High-dimensional data poses challenges for traditional pattern recognition algorithms.
  • Dimension reduction is crucial for effective sample classification.

Purpose of the Study:

  • To present a novel feature extraction algorithm for sample classification.
  • To integrate microarray expression data with Gene Ontology (GO) for improved analysis.
  • To enhance classification accuracy in biological studies.

Main Methods:

  • Developed a novel feature extraction algorithm by integrating gene expression data with Gene Ontology.
  • Utilized semantic similarity measures to identify functionally related gene groups (virtual genes).

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Performing Custom MicroRNA Microarray Experiments
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Last Updated: Jun 21, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

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Published on: July 29, 2022

Performing Custom MicroRNA Microarray Experiments
07:04

Performing Custom MicroRNA Microarray Experiments

Published on: October 28, 2011

  • Employed correlation in virtual gene expression for sample classification.

    • Main Results:

    • The proposed method significantly improved classification accuracy.
    • Classification accuracy for colon cancer data was enhanced by over 10%.
    • Identified 'virtual genes' as effective features for sample classification.

    Conclusions:

    • The novel feature extraction method integrating gene expression and GO is effective.
    • This approach offers a significant improvement in sample classification accuracy.
    • The 'virtual gene' concept provides a promising direction for bioinformatics research.