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The clear formulation of the problem and performance measures allow us to precisely analyze and compare existing DR-planners that use two well-known types of decomposition methods: SR (constraint shape recognition) and MM (generalized maximum matching) on constraint graphs.
Co-morbidity measures allow us to efficiently evaluate the disease burden of large cohorts of patients using existing data, such as administrative encounter and pharmacy dispensing records.
These fitness measures allow us to take kin competition into account.
These measures allow us to distinguish between the mutual dependent contribution and the independent contribution of an input to the model response variance.
Combining the S and C measures allow us to classify each external host as a scanner or benign host.
The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes.
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The methodology of mixed modeling for repeated measures allows us the use of flexible error variance-covariance structure.
Including both implicit and explicit measures allows us to use these initial findings to make predictions on those scenarios where implicit rather than explicit liking may lead to social patterning in behaviour.
As expressed in our theoretical model, and in accordance with Kelley-Gillespie, 47 the simultaneous use of these two measures allows us to analyze both dimensions.
In addition, the use of multiple ABR measures allowed us to examine relationships between different ABR constructs in relation to breast cancer outcomes.
The use of a synthetic index rather than using independent ecological socio-economic measures allows us to represent an accumulation of social and material disadvantages [ 45] and these kinds of indexes are more useful to lead population interventions [ 27].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com