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High-dimensional additive hazards regression for oral squamous cell carcinoma using microarray data: a comparative

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  • 1Department of Science, Hamadan University of Technology, Hamedan 65155, Iran.

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Sparse models are crucial for analyzing high-dimensional gene expression survival data. This study found that Lasso and smoothly clipped absolute deviation effectively identified influential genes, improving survival prediction accuracy.

Area of Science:

  • Bioinformatics
  • Genomics
  • Statistical Modeling

Background:

  • Microarray data presents challenges due to high dimensionality and low sample size.
  • Identifying influential genes is critical for accurate survival time prediction.
  • Sparse modeling techniques are essential for handling such data.

Purpose of the Study:

  • To evaluate three sparse variable selection techniques: Lasso, smoothly clipped absolute deviation (SCAD), and the smooth integration of counting and absolute deviation (SICA).
  • To apply these methods to gene expression survival time data using the additive risk model.
  • To assess the predictive performance of these techniques.

Main Methods:

  • Application of Lasso, SCAD, and SICA for variable selection in high-dimensional time-to-event data.

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  • Utilizing the additive risk model for analyzing gene expression survival data.
  • Performance evaluation using time-dependent ROC curves and bootstrap prediction error curves.

    • Main Results:

    • All selected genes were highly significant (P < 0.001).
    • Lasso achieved the highest median area under the ROC curve (0.95).
    • SCAD demonstrated the lowest prediction error (0.105).
    • Selected genes improved the predictive accuracy compared to a purely clinical model.

    Conclusions:

    • Sparse variable selection techniques effectively identify influential genes from microarray data.
    • These methods significantly enhance survival time prediction accuracy.
    • Gene expression data holds valuable information for improving clinical predictions.