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We have used our SNP diversity dataset to develop and optimize several 384-SNP genotyping assays for use in applied breeding programs.
We applied the Pilot dataset to develop and validate a method for clustering and mapping objectively detected potentially ligandable binding sites.
Rather, we combined all patients into one dataset to develop and validate POD composite outcome measures.
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(i) The development of fast simulation tools for high resolution sensors: this will enable us to tackle the current lack of real datasets to develop and evaluate new algorithms including generative models for target identification.
The authors have used this dataset to develop NER and relation detection tools, which perform quite well in terms of precision, recall and F-score.
The developed model was validated using leave-out-one cross-validation approach, where randomly (N − 1) observations were selected from the original dataset to develop the model and then tested on the left-out-one observation, which was repeated for N times, where N is the number of observations in the dataset [17].
This dataset was applied as the training dataset to develop our prediction model.> -wrap-foot> *PositiveNum and NegativeNum represent the number of positive samples and negative samples, respectively.
In this study, a three-step approach to predictive analytics was proposed and piloted on an operational clinical dataset to develop predictive models for CHF readmission.
Two researchers recursively read the dataset to develop a preliminary coding structure.
The dataset used to develop and validate the presented model is obtained from the public literature.
Despite analyzing 6 years worth of data, the size of the dataset used to develop and validate our model is rather small.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com