Sentence examples for samples in model from inspiring English sources

Although high-throughput RNA sequencing has recently become popular as an alternative to microarray analysis, the microarray platform, with its robust sample process and data analysis pipelines, is still the preferred choice for transcriptomic profiling involving a large number of samples in model plants with well-annotated genomes [ 14, 15].

Using changes in tDR expression between pre- and post-treatment samples in model fitting would explain less variation in the data (R = 40%%); however, since pre-treatment tRNA expression is a more clinically useful analysis, the better prediction value is fortuitous.

Comparison of Models B & D indicate if the restricted sample used in Model D (observations with air pollution variables) produce different effect estimate than those obtained using the full sample in Model B. aHeat wave indicator denotes any day that is part of 2 or more consecutive days when maximum temperature was above the 95th percentile for the MSA.

Full cross validation is an evaluation tool to check calibration models, based on systematically removing samples in the model and testing the performance of the model using the remaining data set.

We propose a method of identifying a subset of the GLAS data which can justifiably be treated as a simple random sample in model-based biomass estimation.

However, this only happened in less than 3% of the sample in Models 3 and 4 when the Tobit method was used.> -wrap-foot> AbbreviationSD standardardeviationon, Min minimum, P10 10th percentile, Q1 1st quartile, Q3 third quartile, P90 90th percentile, Max maximum.

An incoming pixel value is compared with the samples in the model and is classified as background if its pixel value is closer than a distance threshold R to at least #min of the N samples in the model.

Shadow detection by matching with the samples in the model exploits the recurrence of similar shadow values at pixel level.

When an incoming pixel value belongs to the background, the number of matches observed with the samples in the model will depend on the dynamics of the region.

An input pixel is classified as background if it finds the required number of matches with the samples in the model.

Previous sample-based methods used a global constant value for #min (the number of matches required with the samples in the model to classify an input pixel as background).