title: Classification of mislabelled microarrays using robust sparse logistic regression.
abstract: BACKGROUND
  Previous studies reported that labelling errors are not uncommon in microarray datasets. In such cases, the training set may become misleading, and the ability of classifiers to make reliable inferences from the data is compromised. Yet, few methods are currently available in the bioinformatics literature to deal with this problem. The few existing methods focus on data cleansing alone, without reference to classification, and their performance crucially depends on some tuning parameters.
  
  
  RESULTS
  In this article, we develop a new method to detect mislabelled arrays simultaneously with learning a sparse logistic regression classifier. Our method may be seen as a label-noise robust extension of the well-known and successful Bayesian logistic regression classifier. To account for possible mislabelling, we formulate a label-flipping process as part of the classifier. The regularization parameter is automatically set using Bayesian regularization, which not only saves the computation time that cross-validation would take, but also eliminates any unwanted effects of label noise when setting the regularization parameter. Extensive experiments with both synthetic data and real microarray datasets demonstrate that our approach is able to counter the bad effects of labelling errors in terms of predictive performance, it is effective at identifying marker genes and simultaneously it detects mislabelled arrays to high accuracy.
  
  
  BACKGROUND
  The code is available from http://cs.bham.ac.uk/∼jxb008.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: School of Computer Science, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK. J.Bootkrajang@cs.bham.ac.uk
authors: Jakramate
authors: J
authors: Bootkrajang
authors: Ata
authors: A
authors: Kabán
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism; D003110:Colonic Neoplasms / Q000235:genetics / Q000378:metabolism; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*