Sentence examples for internal dataset from inspiring English sources

After above steps, internal dataset which including 6554 bio-active data with corresponding protein targets (5 major nuclear receptor) and compounds were selected for Proteochemometric modeling.

This means that upon querying an external dataset with an instance ((X_{i} = (x_{mathrm{int},1},ldots,x_{mathrm{int},m}))), we get a new set of features (((x_{mathrm{ext},1'},ldots,x_{mathrm{ext},m'}))), which are added to the feature-space of the internal dataset, resulting in the instance (X_{i} = (x_{mathrm{int},1},ldots,x_{mathrm{int},m},x_{mathrm{ext},1'}, ldots,x_{mathrm{ext},m'})).

Since no external validation dataset is currently available, model performance was assessed using the internal dataset in two approaches.

In relation to the modelling approach adopted it is important to highlight that model performance was validated using the internal dataset only.

Although a major concern in the validation of a microarray-based prediction model is model transferability i.e. the usefulness of a transferred model outside of its intended use, it is also desirable that the signature developed from the internal dataset is applicable to predict future samples.

Through an integrated analysis of literature data and internal datasets incorporating both cell line models and human tumors, we identified a RAS pathway signature consisting of 147 genes that is coherently expressed across multiple datasets.

In addition, the SysPro ontology is being expanded to include concepts from GO and PRO to enable linking of genotype and protein data from external sources with CPB internal datasets.

The prediction models were also validated among 144 participants (32 participants from the internal validation dataset and 112 from the external validation dataset) with undiagnosed diabetes.

In our study, BIC can be written as: BIC = − 2 log L + k log n where 'L' is likelihood of the prediction model in internal validation dataset, 'k' is the number of predictors, and 'n' is the number of samples of internal validation dataset.

For the second experiment we considered the estimation of the LogP for an internal Syngenta dataset.

LASSO showed a similar but slightly lower AUC value than SVM in the internal validation dataset.