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There are many techniques in bioinformatics for the analysis of DNA microarray data; however, these are mainly divided into fold-change analysis, clustering, classification, genetic network analysis, and simulation.
This method has been tailored for microarray data, however its use is being explored also in different fields [37], [38].
Some support for this comes from the microarray data, however, the absolute number of differentially regulated genes was small.
In general, the results obtained by qRT-PCR and Northern blotting were consistent with the microarray data, however, some of the effects observed by microarrays could not be verified by other methods.
Microarray data however often contains many missing values.
Prostate cancer microarray data, however, is not easily classified.
Similar(43)
Analysis of microarray data is however very complex, requiring sophisticated methods to control for various factors that are inherent to the procedures used.
This approach thus provides non-informative microarray data that, however, preserve their realistic correlation structure, and can serve as a "baseline" to quantify the bias induced by optimal classifier selection.
The ultimate goal of microarray data analysis is, however, obtaining sound biological conclusions about which transcripts are involved in a particular process.
When comparing these targets shown in the network to our previous mRNA microarray data [ 9], however, we found that only TLK targeted by miR-205 showed significant up-regulation.
However, microarray data could not distinguish those genes directly controlled by OxyR form those controlled by indirect mechanisms.
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