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Although this is partially overcome by discarding compounds active in several assays of the same project and consequently, performed in similar experimental backgrounds (filter F3), our approach also detects connections between cell-free assays that are apparently unrelated.
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Firstly, in order to improve the speed of detection in the system, we train an Adaboost background filter which can remove the background most quickly.
The following settings were taken from previous studies [4], [5]: a background filter cut-off was defined to lower the count of false positive calls using the highest signal value obtained from non-human Affymetrix control oligonucleotides multiplied by a factor of 1.2, corresponding in the current data set to a log based 2 value of 5.8.
In total 2835 genes were removed by the background filter.
First, we inspected the set of genes that were removed in the analysis with background filter.
This means that only genes removed by the background filter show highly different p values.
In summary, the background filter removes 53% of all candidate genes from the result list.
p values were taken from the ANOVA analysis both without and with the background filter.
Then, we inspected candidate genes that were found only in the analysis with background filter.
To test this hypothesis, we analysed the AS ANOVA p values of genes removed by the background filter and genes that were only found with the background filter (in total 1559 genes).
This background filter resulted in the removal of approximately 55% of all the genes on the Illumina array.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com