Estimation Methods for Microarray Data With Missing Values: A Review

Download Now Date Added: Mar 2011
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DNA microarrays have gained widespread uses in biological studies such as cancer classification, cancer prognosis and identifications of cell cycle-regulated genes of yeast because of their large number of genes and small size. But they often produce missing expression values due to various reasons which significantly affect the performance of any data analysis. One primary concern of classifier learning is prediction accuracy. Presence of incomplete information significantly effect the performance and accuracy of a classifier. Hence, prior to the classification a complete matrix is needed for which in the pre processing step the missing value should be estimated (imputed).