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modelBuildR: an R package for model building and feature selection with erroneous classifications.

Maximilian Knoll1,2,3, Jennifer Furkel1,2,3, Juergen Debus1,2,3

  • 1Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Deutschland.

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|February 22, 2021
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Summary

A novel feature pre-filtering heuristic improves omics-based model building by enhancing the identification of true biological signals, even with erroneous human classifications. This method, implemented in the R package modelBuildR, aids in uncovering robust biomarkers for biomedical research.

Keywords:
Feature selectionG-CIMP negative GBMGlioblastoma multiformeGround truthHigh dimensional dataIllumina humanmethylation array dataLong term/short term survivorMisclassificationModel buildingPrognosis

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Area of Science:

  • Biomedical research
  • Bioinformatics
  • Computational biology

Background:

  • Omics-based research relies on accurate model building for biological insights.
  • Human classifications in biomedical data can be error-prone, impacting model reliability.
  • Feature selection methods often prioritize prediction accuracy over identifying true biological associations.

Purpose of the Study:

  • To evaluate a novel feature pre-filtering heuristic for improving omics-based model building.
  • To assess the impact of feature pre-filtering on identifying features associated with true biological groups, especially when classifications are erroneous.
  • To benchmark the proposed heuristic against standard methods using simulated and real-world data.

Main Methods:

  • Simulated omics data with varying numbers of samples, features, and populations.
  • Comparison of a novel heuristic (V1) with standard cross-validation (V2) for feature preselection.
  • Training of logistic regression/linear models using preselected features.
  • Benchmarking against multiple feature selection/classification methods on TCGA-GBM methylation array data.

Main Results:

  • The novel heuristic (V1) demonstrated superior performance (higher median AUC ranks) compared to standard methods (V2), particularly for binary classifications and two true groups.
  • V1 achieved median AUCs of 0.91 for binary classification (two groups) and 0.75 for larger datasets, outperforming V2 (0.70 and 0.54, respectively).
  • In the TCGA-GBM cohort, the modelBuildR package, utilizing the heuristic, achieved the best prognostic separation of glioblastoma patients.

Conclusions:

  • The proposed heuristic effectively retrieves features associated with true groups, even when classifications contain errors.
  • The R package modelBuildR facilitates the application and comparative evaluation of this novel heuristic.
  • This approach enhances the reliability of omics-based model building and biomarker discovery in biomedical research.