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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Ensemble methods of rank-based trees for single sample classification with gene expression profiles.

Min Lu1, Ruijie Yin2, X Steven Chen3,4

  • 1Division of Biostatistics, Department of Public Health Sciences, Miller School of Medicine, University of Miami, 1120 NW 14th Street, Miami, FL, 33136, USA. m.lu6@umiami.edu.

Journal of Translational Medicine
|February 6, 2024
PubMed
Summary

This study introduces ensemble rank-based trees for disease classification using gene expression data, improving upon existing methods like Top Scoring Pairs (TSP) for more complex patterns.

Keywords:
BoostingDecision treeEnsemble learningRandom forestRank discriminantSingle sample predictor

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Single Sample Predictors (SSPs) face calibration challenges across diverse gene expression technologies.
  • Phenotype classification using gene expression order shows promise, with Top Scoring Pairs (TSP) offering platform independence.
  • Traditional TSP methods struggle with complex patterns involving more than two gene comparisons.

Purpose of the Study:

  • To develop a novel approach extending Top Scoring Pairs (TSP) for enhanced gene expression-based disease classification.
  • To address limitations of existing methods in handling complex gene-gene interaction patterns.
  • To improve the accuracy and interpretability of Single Sample Predictors (SSPs).

Main Methods:

  • Constructing rank-based trees to encompass extensive gene-gene comparisons, extending TSP rules.
  • Incorporating ensemble strategies, specifically boosting (LogitBoost) and random forest, to mitigate overfitting.
  • Implementing ensemble rank-based trees for both binary and multi-class classification problems.

Main Results:

  • Proposed ensemble rank-based trees demonstrated superior performance across 12 cancer gene expression datasets compared to k-TSP and nearest template prediction.
  • The refined approach facilitates variable selection and generates clear, precise decision rules, enhancing interpretability.
  • The method proves robust, interpretable, and scalable for disease classification using gene expression data.

Conclusions:

  • Ensemble rank-based trees offer a significant advancement in disease classification using gene expression data.
  • The developed method provides a robust, interpretable, and scalable solution overcoming limitations of previous SSP techniques.
  • The software package 'ranktreeEnsemble' is available for broader application and research.