title: Methods for labeling error detection in microarrays based on the effect of data perturbation on the regression model.
abstract: BACKGROUND
  Mislabeled samples often appear in gene expression profile because of the similarity of different sub-type of disease and the subjective misdiagnosis. The mislabeled samples deteriorate supervised learning procedures. The LOOE-sensitivity algorithm is an approach for mislabeled sample detection for microarray based on data perturbation. However, the failure of measuring the perturbing effect makes the LOOE-sensitivity algorithm a poor performance. The purpose of this article is to design a novel detection method for mislabeled samples of microarray, which could take advantage of the measuring effect of data perturbations.
  
  
  RESULTS
  To measure the effect of data perturbation, we define an index named perturbing influence value (PIV), based on the support vector machine (SVM) regression model. The Column Algorithm (CAPIV), Row Algorithm (RAPIV) and progressive Row Algorithm (PRAPIV) based on the PIV value are proposed to detect the mislabeled samples. Experimental results obtained by using six artificial datasets and five microarray datasets demonstrate that all proposed methods in this article are superior to LOOE-sensitivity. Moreover, compared with the simple SVM and CL-stability, the PRAPIV algorithm shows an increase in precision and high recall.
  
  
  BACKGROUND
  The program and source code (in JAVA) are publicly available at http://ccst.jlu.edu.cn/CSBG/PIVS/index.htm
authors: College of Computer Science and Technology, Jilin University, 130012 China.
authors: Chen
authors: C
authors: Zhang
authors: Chunguo
authors: C
authors: Wu
authors: Enrico
authors: E
authors: Blanzieri
authors: You
authors: Y
authors: Zhou
authors: Yan
authors: Y
authors: Wang
authors: Wei
authors: W
authors: Du
authors: Yanchun
authors: Y
authors: Liang
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated