Two-stage classification methods for microarray data
Expert Systems with Applications: An International Journal
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
ACST'07 Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology
A Comparison of Fuzzy Clustering Approaches for Quantification of Microarray Gene Expression
Journal of Signal Processing Systems
A mixture model approach for the analysis of small exploratory microarray experiments
Computational Statistics & Data Analysis
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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Motivation: 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. Availability: The classification approaches described in this paper and sample microarray data are available as MatlabTM (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. Contact: asyali@kfshrc.edu.sa