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This article advances a strategic proposal that would enable future research in data/knowledge engineering and natural language processing to take a broader range of meanings into account than can be derived from analyzing text with current methods focusing on syntactic and semantic text meanings.
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When simultaneously evaluating multiple predictors for disease outcome, current methods focus on building multivariable models, rather than the evaluation of single exposures adjusting for known covariates [ 15].
Current methods focus on the implementation of such projects.
Current methods focus on the discovery of single nucleotide polymorphisms (SNPs) associated with disease traits.
However, most of the current methods focus on the detection of co-complex interactions and do not discriminate between direct physical interactions and indirect interactions.
Current phage biopanning methods focus on identifying peptides that bind to and/or mediate cellular uptake.
Most current SNP imputation methods focus on methods applied in high-density human SNP-typing studies, where a reference panel may be available but where phase inference is a source of error [ 33].
A review of current literature revealed that overall, presently available methods focused on single ENP mostly and were applicable mainly to samples of low organic loadings or under well-controlled laboratory conditions.
It critically improves current methods by focusing on the decision process introducing a logit Eq. (3).
We highlight the need to extend the application of current methods to focus on a greater range of habitats and mycorrhizal types enabling incorporation of mycorrhizal fungal biomass and turnover into biogeochemical cycling models.
Current methods usually focus on the assessment of single chemicals.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com