validation set

validation set

Decision-making A group of Pts with a clinical finding of interest–eg, chest pain, who are studied prospectively in order to verify facets of their disease that had been previously identified as possible predictors of outcome. See Derivation set.
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Tenders are invited for cots based/ customised platform based solution for design, development, implementation and maintenance of capi enabled general survey instruments(cagsi) with real tiime data validation set up for nss survey (present and future) at dpd mospi
tuberculosis n = 298 RIVM collection of genomes of animal species augmented with 5 published genomes for rare animal strains n = 20 + 5 published strains Validation set: n = 614 Published animal strains n = 103 Genomes from PHE collection selected on basis of predicted species using new algorithm, confirmed using maximum-likelihood phytogeny n = 511 Clinical set: N = 3,128 Clinical set--predictions made using new validated algorithm to estimate caseload of animal subspecies in the PHE laboratory in Birmingham, UK n = 3,128 All newly sequenced genomes are available from the National Center for Biotechnology Information under project accession no.
Notably, this validation set also included samples collected from children at the University of California, Irvine, to verify that the RNA algorithm performed accurately in samples from different geographic regions.
In the validation set, serum CKAP4 levels were also significantly higher in lung cancer patients than in healthy controls.
Peripheral blood samples for the training set (n = 60) and for validation set I (n = 44) were obtained from the Health Effects in High-Level Exposure to PCB (HELPcB) program (28).
Tables 2-5 show the average and the standard deviation of the classification rate on the validation set for the ALS versus healthy, HD versus healthy, PD versus healthy, and NDDs versus healthy.
At each training run, 4/5 of the dataset were used as a training set and the remaining 1/5 was used as the validation set. Subsequently, the final model network was adjusted and tested for performance using the dataset divided into three sets: training, validation, and test.
where [??] and Y are the output variables of validation set and calibration set, [[PHI].sub.v] is the matrix of validation variable feature space mapping, latent vectors T and U are linear combinations of input and output variables, and [K.sub.v] is the matrix composed of [K.sub.ij] = K([x.sub.i], [x.sub.j]), where [x.sub.i] and [x.sub.j] are input variables of validation set and calibration set.
Since GSE67139 has a larger sample size than GSE61738, we regarded GSE67139 as the discovery set and GSE67138 as the validation set.
Thirty-five of these samples were selected for the calibration set and five samples were chosen for the validation set.
The models were also validated by prediction of the concentration of analytes in separate validation set which was not used in the model development (Table 2).