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For different picks of data samples from our training set, this procedure is repeated iteratively until a convergence occurs.
In the proposed approach, an M-estimate winsorization method is applied to the transformed data set: this procedure eliminates the effects of outliers in the training data set, while retaining the multivariate structure of the data.
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For each subsample in the series, the individual that is most different from those selected from previous subsamples is extracted and added to the selection set S. This procedure results in a representative but diverse selection set that samples the full data set space both efficiently and effectively [42].
After compiling the training set with this procedure for every structural superclass, the SVR is applied to each training set as described above.
The only parameter we need to set in this procedure is the number of dimensions X to preserve when performing SVD.
Second, five complete-data analyses are performed by treating each completed-data set as an actual complete-data set; this permits standard complete-data procedures and software to be utilized directly.
The parameter set obtained through this procedure is reported in Table 3.
After analyzing the whole query set (777,585 proteins), this procedure predicted the location of active-site residues for 90,482 proteins (11.6%).
In addition, to avoid poor local minima for a given training set, we repeat this procedure N times and select the model with the highest likelihood (N = 50 in the experiments described below).
Electronic capture of symptomatic toxicities using PRO-CTCAE and the submission of self-reports to clinicians prior to consultation were feasible among metastatic prostate cancer patients receiving chemotherapy in an outpatient setting, and this procedure was acceptable to both patients and clinicians.
We added systematic goal-setting to this procedure.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com