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Updated: May 14, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Classification of mislabelled microarrays using robust sparse logistic regression.

Jakramate Bootkrajang1, Ata Kabán

  • 1School of Computer Science, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK. J.Bootkrajang@cs.bham.ac.uk

Bioinformatics (Oxford, England)
|February 19, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for detecting mislabelled microarray data by integrating label noise detection into sparse logistic regression classification. The approach accurately identifies mislabelled arrays and improves predictive performance, even with noisy data.

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Last Updated: May 14, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Bioinformatics
  • Machine Learning
  • Genomics

Background:

  • Microarray datasets are susceptible to labelling errors, compromising classifier reliability.
  • Existing methods for handling label noise in bioinformatics are limited and often require parameter tuning.
  • Accurate data labelling is crucial for reliable biological data analysis and inference.

Purpose of the Study:

  • To develop a robust method for detecting mislabelled arrays concurrently with learning a sparse logistic regression classifier.
  • To address the limitations of existing data cleansing methods by integrating label noise handling into the classification process.
  • To provide a computationally efficient and parameter-free approach for handling label noise.

Main Methods:

  • A novel label-noise robust extension of Bayesian logistic regression is formulated.
  • A label-flipping process is incorporated into the classifier to account for potential mislabelling.
  • Bayesian regularization is employed for automatic setting of the regularization parameter, avoiding cross-validation.

Main Results:

  • The proposed method effectively detects mislabelled arrays with high accuracy.
  • It significantly improves predictive performance on datasets with labelling errors.
  • The approach is effective in identifying marker genes and demonstrates robustness against label noise.

Conclusions:

  • The developed method offers a powerful solution for handling label noise in microarray data analysis.
  • It enhances the reliability of classification and marker gene identification in the presence of labelling errors.
  • The automatic regularization setting and integrated approach offer computational advantages over existing methods.