title: Reliability analysis of microarray data using fuzzy c-means and normal mixture modeling based classification methods.
abstract: BACKGROUND
  A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. Therefore, the elimination of unreliable signal intensities will enhance reproducibility and reliability of gene expression ratios produced from microarray data. In this study, we applied fuzzy c-means (FCM) and normal mixture modeling (NMM) based classification methods to separate microarray data into reliable and unreliable signal intensity populations.
  
  
  RESULTS
  We compared the results of FCM classification with those of classification based on NMM. Both approaches were validated against reference sets of biological data consisting of only true positives and true negatives. We observed that both methods performed equally well in terms of sensitivity and specificity. Although a comparison of the computation times indicated that the fuzzy approach is computationally more efficient, other considerations support the use of NMM for the reliability analysis of microarray data.
  
  
  BACKGROUND
  The classification approaches described in this paper and sample microarray data are available as Matlab( TM ) (The MathWorks Inc., Natick, MA) programs (mfiles) and text files, respectively, at http://rc.kfshrc.edu.sa/bssc/staff/MusaAsyali/Downloads.asp. The programs can be run/tested on many different computer platforms where Matlab is available.
  
  
  BACKGROUND
  asyali@kfshrc.edu.sa.
authors.affiliation: Department of Biostatistics, Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Center PO Box 3354, MBC-03, Riyadh 11211, Saudi Arabia. asyali@kfshrc.edu.sa
authors.forename: Musa H
authors.initials: MH
authors.lastname: Asyali
authors.forename: Musa
authors.initials: M
authors.lastname: Alci
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity