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Hybridization-based microarray technology, due to its high throughput and affordability, is widely used for mapping gene expression patterns [2], [3], transcriptional activities (genome tiling array) [4] [6], and binding sites of regulatory proteins (ChIP-on-chip) [7].
It is commonly believed to be superior to microarray technology due to its ability to quantify gene expression at higher resolution (exon and CDS level) and detect structural variations.
Several recent studies have indicated that RNA-seq will be more useful than the current microarray technology due to the increased dynamic range of signal of sequencing [ 4, 26- 28] and its ability to identify the exact location of transcription boundaries at single base resolution.
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Researchers cannot effectively benefit from the current microarray technology completely due to limitations of the algorithms being used for data analysis.
Moreover, RT-PCR may be more feasible in the clinical setting than microarray-based technologies due to the need for specialized laboratory facilities and complex statistical analysis [15] [19].
This high AUC value may not be due to the microarray technology because any technology is unreliable at the low intensity/expression end.
All data were normalized to remove from the expression measures any systematic bias which arise due to the microarray technology itself, rather than differences between the probes or between RNA samples targets hybridized to the arrays.
The microarray technology is well suited for diagnostic applications due to its highly parallel architecture.
We found a relatively small number; 88 genes were differentially regulated in both short fiber mutants, which may be due to limitations of microarray technology.
Due to the success of microarray technology, the available data on the transcriptional regulatory networks of different organisms has grown exponentially.
We conclude that the differences between these results were mainly due to the limitation of the microarray technology.
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