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In order to choose or optimize microarray methods several criteria can be used.
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To compare measurements across a range of amplification methods, several microarray platforms and other quantification methods, such as qPCR and massive parallel sequencing, we used a uniform method to analyze performance.
RNA-Seq technology has several advantages over DNA microarray methods for transcriptional analysis.
After these primary publications, several groups have reported further development and application of different types of cell microarray methods [ 12- 20].
To overcome the limitations of microarray experiments, several computational methods have been proposed as compensatory methods to find cell cycle regulated genes in yeast.
Keeping up with the pace of development of DNA microarrays are several methods of RNA isolation from tissues and cells, the critical first step in the generation of data.
There has been a great deal of expectation that high-throughput sequencing technologies would allow a global and unbiased characterization of the transcriptome compared with microarrays and, consequently, several methods have been proposed for inferring transcript structures using sequence data.
Regarding time-course microarray data, several model-based clustering methods have been proposed (Luan and Li, 2003; Ramoni et al., 2002; Wu et al., 2005).
This methylation-sensitive technique, termed RMEAM (regional methylation elongation assay on microarray), provides several advantages over existing methods used for methylation analysis.
The array-MAPH methodology offers an alternative methodology to array-CGH and provides a new sensitive microarray-based method including several advantages for the detection of copy number changes in the human genome.
This method requires data from many microarray experiments (several hundreds).
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