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In our view, the most important criterion to evaluate a method of clustering metagenomic data is robustness.
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An important feature of any predictor based on gene expression data is its robustness with respect to the choice of dataset, since gene expression data from cancer patients come from studies using different protocols and/or microarray platforms.
Another issue for modularity is robustness.
Another type of robustness is robustness to mutations.
We have demonstrated the efficacy of our method with synthetic and experimental data, where the main purpose of synthetic data is to profile the robustness and precision of the proposed method.
Further, an experimental data is used to validate the robustness of the formulated GP model.
The reliability of normalized data is highly dependent on RG robustness.
Robustness to missing data is tackled by means of simple imputation (data replacement) schemes, such as exponential forecasts and spatial interpolation.
An assessment of its robustness, using synthetic data, is given.
The robustness of our data is supported by high trait heritabilities (typically H2>0.7) and significant correlations of trait values measured in baseline condition with independent multistrain datasets of the Mouse Phenome Database.
Due to the collection methods used, the robustness of the data is dependent on the accuracy and extent of the information recorded in the database.
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