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Data reduction methodologies are of particular importance to medicine since most secondary clinical databases contain only the data parameters of interest (Wang and Krishnan 2014).
Pure subtractive methodologies are of limited use due to the need for a large quantity of mRNA to drive hybridization to completion as well as the difficulty in cloning the tiny amount of cDNA remaining after hybridization.
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Therefore the publication of successful methodologies is of particular interest to those in the field with aspirations to translate their basic studies into the clinic.
The research methodology is of qualitative nature.
In this context, the choice of methodology is of paramount importance.
Given that normative concepts are the focus of much philosophical practice, one might worry that the methodology is of limited scope.
The methodology is of substantive interest since the effects of other covariates are jointly estimated with the random effects, e.g., spatially structured and unstructured heterogeneity effects [ 16].
We investigated how mtDNA diversity is distributed across geography within L. rufescens following [ 65]; this methodology is of particular use when localities have unequal sample sizes.
The methodology is of potential interest, in the effort to infer the state of individual pathways and modules based on microarray data analysis.
Consequently, when evaluating risk, deterministic methodologies are only of limited value.
Diagnostic methodologies are the core of HUM systems.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com