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In this paper, the training stage is always carried out with a one-versus-rest strategy between the positive set (the samples in the target language) and negative set (all other samples).
Our study design had three samples in each set; the samples consisted of DNA from blood, tumor, and adjacent normal tissue from the same individual.
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In our current implementation, we set the sample size to 10%, for performance and scalability reasons.
On Easy-nLC 1000 UHPLC, set the sample loading and RP gradient method as follows.
We set the sample size N = 1000, and the simulation results are achieved by 20,000 Monte Carlo simulations.
We set the sampling epoch to one minute.
Given the above, we set the sample size according to the Provisions for Drug Registration.
We set the sample size as the number of site patterns in the alignment.
To allow for 10% patient ineligibility, we set the sample size of our study at 110.
Set the sample chamber to 8 °C and injection volume at 10 μl.
Load inlet method and set the sample chamber to 8 °C and injection volume at 5 μl.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com