Sentence examples for samples to rank from inspiring English sources

Random forest (RF) associated with variable importance measurements (VIMs), which is a new powerful statistical data mining approach, is utilized in this study to analyze a high-dimensional database (3961 samples) to rank variables, and to develop an accurate FSI predictive model based on the most important variables.

In order to show the belief propagation of our new ranking algorithm, along the manifold of the data, we generated a set of toy data, selected one data point as a query sample, and queried the rest samples to rank them.

This indicated a greater contribution of between-child variability, but still such a substantial contribution of within-child variability that authors expressed concern about the utility of using one sample to rank participants' 48-hr exposure levels (Egeghy et al. 2011).

GeNorm v3.4 software was used to analyse the expression stability of tested genes in various samples, and to rank them accordingly.

We describe the theory behind a novel monitoring methodology that utilizes cross-fence sample pairs to rank paddocks, whether adjacent or not, using a system of linear equations and field based or preferably remotely-sensed data.

Mark-recapture results are assessed using the small sample size corrected information criteria AICc (AICc = AIC + 2 k(k+1)/(n− k−1), where k = the number of parameters and n = sample size) to rank the explanatory ability of different models (Anderson 2010).

A GenCall score, estimated for each datapoint (SNP × individual sample), is designed to rank particular DNA samples or SNP loci and is obtained by the product of the GenTrain Score and a data-to-Bayesian-model fit score as implemented by the Genome Studio software.

It has been shown [14], [15] that if a training set of ∼100 early breast cancer samples is used to rank ∼10,000 genes (by their correlation with outcome), and the ∼100 top genes are selected as the prognostic set, repeating the procedure (with a different set of training samples) will produce a new gene list, whose overlap with the first one is typically 2 3%.

During that time, the organ sharing network will begin collecting blood samples to begin ranking patients.

When using rank order correlation of two data sets that may contain many NDs, assigning the half SLOD value to NDs causes nondetected samples to be ranked according to their process volumes which would falsely skew test results.

We first evaluated the performance of our miRank algorithm on H.sapiens data based on the known miRNA precursors embedded in the pool of samples to be ranked (Section 4.2).