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Because our dataset was large ( >200,000 locations from 2011 2016) and predictive modeling can be computationally expensive, we randomly divided our dataset into fourths and then BRTs were run on each of the four partitions.
However the sample size in this dataset was large.
As the dataset was large, all the potential factors were included in the model.
Our dataset was large (24 180 observations), recent (October 2006 to January 2009) and covered the whole of England.
At this stage, the dataset was large (containing 1260 hits) and comprised mainly of recently expanded gene families.
The dataset was large, and the overall percentage of missing data acceptable and small compared with other methods of data collection.
Similar(49)
Simple neural network architecture with a single hidden layer of five tangent sigmoid transfer functions was performing as good as more complex architectures if the training dataset was larger than ten times the number of coefficients to tune.
This dataset was larger than the one produced here, and it included extreme genetic perturbations.
The EST dataset was larger compared to the microarray dataset, therefore we might have obtained expression evidence for more genes using the EST dataset.
In 48% (14/29) of the meta-analyses the average number of events per dataset was larger for observational studies than for randomised controlled trials.
As in other CNV studies using aCGH experiments [ 15, 20, 25, 26], the number of loss events in our dataset was larger than the number of gain events.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com