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Before introducing the methods, we define some required mathematical concepts here.
In order to compare the different methods, we define the relative distance between the solutions given by each method as: d_{rel}=frac{||hat{x}-tilde{x}||_{2}}{||hat{x}||_{2}} (35).
To determine the accuracy and precision of these methods, we define expressions for their mean and variance.
In order to precisely characterize the performance of different learning methods, we define several performance measures below (see [ 25]).
Based on a statistical evaluation of the distances between adjacent tDNAs (see Methods), we define two tDNAs to be clustered in the genome if they are located within 1000 nt.
When we measure prediction accuracy of the four methods, we define precision as the number of known protein pathogen associations out of all predicted associations and recall as the number of known associations that we predict out of all known associations.
Similar(53)
The control methods we defined, being tangible and practical, were fairly straightforward to determine.
For both recording methods, we defined a pass as a sequence of at least two successive echolocation pulses [37].
Similarly to previous methods, we defined these attributes using the sequential distance between SAP and the nearest functional sites (if greater than 50, set 50 as the upper bound).
Following QC for genotypes successfully called and exclusion of any poor quality DNA and ethnicity outliers as described in Methods, we defined a clean data set of 1,693 MDD cases and 1,697 controls at low probability for MDD, 1,600 controls from a publically available psoriasis case:control study in dbGaP, and 1,209 parents from a ADHD parent-offspring project also in dbGaP.
To facilitate the comparison between different methods, we defined all the single SNPs outside blocks to be blocks by themselves.
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