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Related Experiment Video

Updated: Jun 16, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Identification of regulatory modules in time series gene expression data using a linear time biclustering algorithm.

Sara C Madeira1, Miguel C Teixeira, Isabel Sá-Correia

  • 1Universidade da Beira Interior, Covilhã, KDBIO Group, INESC-ID, Lisbon, Portugal. smadeira@kdbio.inesc-id.pt

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|February 13, 2010
PubMed
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This study introduces CCC-Biclustering, an efficient algorithm for finding gene expression patterns in time series data. It identifies coherent biclusters, aiding in the discovery of gene regulatory modules.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Biclustering gene expression data is computationally challenging (NP-hard).
  • Identifying coherent patterns across genes and time points is crucial for understanding biological regulation.
  • Previous methods often lack efficiency or scalability for large datasets.

Purpose of the Study:

  • To develop an efficient algorithm for finding maximal biclusters with contiguous columns in time series gene expression data.
  • To enable the discovery of coherent expression patterns shared by genes over consecutive time points.
  • To provide tools for ranking and filtering biclusters for biological relevance.

Main Methods:

  • Developed the Contiguous Column-based Coherent Biclustering (CCC-Biclustering) algorithm.

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Last Updated: Jun 16, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

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

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  • Utilized matrix discretization and suffix tree-based string processing for linear time complexity.
  • Implemented statistical significance ranking and redundancy filtering methodologies.

    • Main Results:

    • The CCC-Biclustering algorithm achieves linear time complexity relative to the expression matrix size.
    • Demonstrated effectiveness on both synthetic and real transcriptomic data.
    • Successfully identified known regulatory modules in Saccharomyces cerevisiae under heat stress.

    Conclusions:

    • The proposed algorithm provides an efficient and effective approach for biclustering time series gene expression data.
    • CCC-Biclustering facilitates the discovery of gene regulatory modules and aids in studying responses to environmental stresses.
    • This methodology is valuable for advancing our understanding of complex biological systems.