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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Gene Selection with Sequential Classification and Regression Tree Algorithm.

Caleb D Bastian1, Grzegorz A Rempala2

  • 1Program in Applied and Computational Mathematics, Princeton University, Fine Hall, Washington Road, Princeton, NJ 08544.

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|November 4, 2014
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Summary
This summary is machine-generated.

A new tree-based sequential CART (S-CART) method efficiently selects relevant genes from high-dimensional biological data. S-CART outperforms complex methods in speed and accuracy, simplifying analysis of gene expression datasets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-dimensional data in genomics, like gene expression, presents challenges for variable selection.
  • Sophisticated methods are often computationally expensive and require extensive pre-processing.
  • There is a need for computationally inexpensive, non-parametric screening procedures for rapid gene selection.

Purpose of the Study:

  • To introduce and evaluate a tree-based sequential CART (S-CART) approach for variable selection in binary classification.
  • To compare S-CART's performance against established methods like Random Forest (RF), Bayesian stochastic search variable selection (SSVS), and LIMMA.
  • To demonstrate S-CART's utility in analyzing simulated and real biological data, including gene expression from a mouse study.

Main Methods:

  • Developed a tree-based sequential CART (S-CART) algorithm for variable selection.
  • Compared S-CART against RF, SSVS, and LIMMA using simulated data based on a hierarchical Bayesian model.
  • Evaluated selection efficacy using false-discovery and missed-discovery rates.
  • Applied S-CART to a control-treatment mouse study involving gene expression data.

Main Results:

  • S-CART consistently outperformed SSVS and RF in both speed and detection accuracy across various simulation scenarios.
  • Network analysis using S-CART-selected genes recapitulated biological findings from the mouse study with a fraction of the original genes.
  • S-CART demonstrated efficient extraction of relevant information from high-dimensional, potentially redundant datasets.

Conclusions:

  • Simple gene selection algorithms like S-CART are often preferable to more sophisticated methods in practical settings.
  • S-CART's greedy selection approach scales well with large datasets and requires minimal tuning.
  • S-CART offers an efficient solution for extracting biologically relevant information from complex gene expression data.