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Updated: May 20, 2026

Cost-Efficient Transcriptomic-Based Drug Screening
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Cost-Efficient Transcriptomic-Based Drug Screening

Published on: February 23, 2024

Statistical knockoffs improve biomarker discovery from transcriptomic data.

Julie Cartier1,2,3, Johanna Lagoas1,2,3, Youmna Ayadi1,2,3

  • 1Centre for Computational Biology, Mines Paris, PSL University, 60 bd Saint-Michel, 75272 Paris, France.

Briefings in Bioinformatics
|May 19, 2026
PubMed
Summary
This summary is machine-generated.

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The knockoff (KO) procedure effectively identifies important genetic predictors in high-dimensional transcriptomic data for classification. This method is more conservative than others, preventing overestimation of relevant features.

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • High-throughput sequencing generates vast biological data, enabling gene-phenotype association studies.
  • Variable selection in high-dimensional, correlated data (p >> n) presents significant challenges for identifying true associations.

Purpose of the Study:

  • To evaluate the knockoff (KO) procedure for variable selection in high-dimensional transcriptomic classification.
  • To assess the KO framework's performance against established variable selection models using simulated and real data.

Main Methods:

  • The study applied the knockoff (KO) variable selection procedure, designed to control the false discovery rate while accounting for variable correlations.
  • Extensive simulations were conducted using real transcriptomic data to test the KO framework in a classification context.
Keywords:
knockoffstranscriptomicvariable selection

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  • KO aggregation was explored to enhance the stability of KO results.

    • Main Results:

    • The KO framework demonstrated superior performance compared to widely used variable selection models in high-dimensional classification.
    • KO aggregation improved the stability of findings without compromising statistical power.
    • Application to real transcriptomic datasets revealed that the KO framework is conservative, making few discoveries.

    Conclusions:

    • The knockoff (KO) procedure is a robust method for variable selection in high-dimensional transcriptomic classification.
    • The KO framework's conservative nature suggests it is reliable in identifying truly relevant features, unlike methods that may overestimate feature importance.
    • KO aggregation offers a strategy to improve the stability of variable selection results.