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This multiplex provided more meaningful data through interrogation of the same experimental samples, thereby providing an internally controlled dataset.
In this way, the real-time cell viability assay could be used to normalize the signal of other assays (e.g., reporter assays) to cell number in each well, providing an internally controlled dataset.
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This method was tested on controlled datasets (i.e., true values of estimates were known a priori) and found effective in estimating these end members and mixing proportions.
Intriguingly, unstructuredness was not significantly enriched (at 95% level of significance) in the "control dataset 3", which comprised of proteins implicated in breast cancer, compared to "control dataset 1"; whereas in comparison to "control dataset 2" it was significant.
When compared to "control dataset 2", unstructuredness was found to be significantly prevalent in all the disease datasets.
Similar trend was observed in breast cancer proteins when "control dataset 3" was compared to "control dataset 1" although it is reported that unstructuredness is prevalent in cancer [6].
The third control dataset, "control dataset 3", consisted of 117 human proteins obtained using SwissProt sequence retrieval system (SRS) by searching the query "[libs = {swiss_prot trembl}-Organism: homo sapiens*] & [libs-Description: breast* & cancer*] " (see Table S8).
"Control dataset 1" comprised of 17159 hits from SwissProt (release 55.0), "control dataset 2" comprised of 264 human enzymes which have known PDB structures and "control dataset 3" consisted of 117 human proteins implicated in breast cancer which were also derived from SwissProt.
This first one, "control dataset 1" consisted of 17159 human proteins obtained using SwissProt sequence retrieval system (SRS) by searching the query "[swiss_prot-Organism: homo sapiens*] !
Two hundred bias-controlled datasets were generated using different random values.
The control samples with invalid traffic data would also be removed from the control dataset.
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