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In Experimental Application I, we used SWISS to address the one-color versus two-color microarray problem on a variety of levels.
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Limma calculates moderated t-statistics, adding to the error term some information on the variance of all genes, solving the typical microarray problem of small sample size (2 in one of the experimental groups).
We will use SWISS to evaluate the one-color versus two-color microarray problem.
In this article, a sparse compact incremental learning machine (SCILM) is proposed for cancer classification problem on microarray gene expression data, which take advantage of correntropy cost that makes it robust against diverse noises and outliers.
Recognizing this problem, on Mar.
For microarrays, this problem is multiplied by the number of genes on the microarray itself.
These methods are common used in microarray classification problems [ 49- 51].
Face the problem head-on.
To address this problem, we performed protein binding microarray experiments on representatives of canonical TF families in C. elegans, obtaining motifs for 129 TFs.
Seven of these 27 failed the validation step, because these genes showed no expressions in the 63 samples, indicating microarray artifacts or problems with the Assay-on-Demand TaqMan® probes (Table 2).
Plant researchers can use MAMA to predict cis-motifs from microarray data on a single treatment.
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