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Related Experiment Video

Updated: Jun 23, 2026

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response
09:45

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

Published on: August 10, 2017

Spectral preprocessing for clustering time-series gene expressions.

Wentao Zhao1, Erchin Serpedin, Edward R Dougherty

  • 1Electrical and Computer Engineering Department, Texas A&M University, College Station, TX 77843, USA.

EURASIP Journal on Bioinformatics & Systems Biology
|April 22, 2009
PubMed
Summary
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This study introduces a new clustering method for gene expression data that uses time information. It effectively groups genes involved in time-regulated biological processes, outperforming traditional methods.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Gene expression profiles are used to cluster genes into groups.
  • These clusters may represent biological processes like cell cycle or circadian rhythms.
  • Traditional clustering methods often overlook temporal information in gene expression data.

Purpose of the Study:

  • To propose a novel clustering preprocessing strategy for time series gene expression data.
  • To fully exploit the time information inherent in gene expression profiles.
  • To improve the grouping of genes involved in time-regulated biological processes.

Main Methods:

  • A novel clustering strategy combining spectral estimation techniques with traditional clustering.
  • Application of the strategy to time series gene expression data.

Related Experiment Videos

Last Updated: Jun 23, 2026

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response
09:45

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

Published on: August 10, 2017

  • Comparison of results with biologically annotated yeast cell-cycle genes.

    • Main Results:

    • The proposed clustering strategy yields significantly different clusters compared to traditional expression-based schemes.
    • The technique effectively groups genes participating in time-regulated processes.
    • Validation using yeast cell-cycle genes confirms the strategy's efficacy.

    Conclusions:

    • The novel clustering strategy effectively utilizes temporal information in gene expression data.
    • This approach enhances the biological relevance of gene clusters, particularly for time-dependent processes.
    • The method offers an improvement over existing expression-based clustering techniques for analyzing dynamic biological systems.