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During statistical analysis of diurnal microarray data, there are many challenges to correctly identify the subset of genes with a clear diurnal signature.
In contrast to mRNA microarray data, there can be multiple probe-sets representing the same miRNA.
According to the microarray data, there is a highly variable genomic region upstream of the deletion of interest in 19q13.31.
Usually in microarray data, there are many groups of genes having similar expression patterns and the test statistics (for example, t-statistic) are not independent within one group.
Although RNA sequencing (RNA-seq) data could be more accurate than microarray data, there is currently little RNA-seq data available for most bacterial species.
However, given the large number of variables compared to the small sample size of microarray data, there are only a small number of effective genes that can be identified reliably [6].
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There was good correlation between replicate experiments of oligonucleotide microarray data, but there was less coherence in expression profiles as measured by Serial Analysis of Gene Expression and Expressed Sequence Tag counts.
Of these 189 microarray data sets, there are 104 high-density human gene expression arrays from HG17 assembly.
Similar to the early microarray data, however, there has been a lack of large RNA-Seq datasets with the necessary technical replicates.
Classification ranking is a challenging problem, particularly for microarray data, where there is a huge number of possible regulated genes with no known rating function.
Our hypothesis suggests that, in many microarray data sets, there is substantial additional biological information contained in the features that remain after the first optimal set is removed.
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