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These methods are compared to linear and nonlinear principal component analysis (autoencoders) in the context of data visualization; these approaches may perform poorly for visualization when the variance of the data is spread across more than three dimensions.
Findings generated from this study will have particular relevance in understanding how evidence-based approaches may perform with such populations in everyday clinical environments.
Recently, there has been some debate as to the universality of the claim that these aggressive approaches are optimal for minimizing the risk of resistance, with some researchers suggesting that more moderate approaches may perform better (Read et al., 2011) and others defending the standard approach (Ankomah and Levin, 2014).
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The What Works Centre at the London School of Economics considered 700 published studies on business advice, suggesting: "Programmes that used a hands-on, managed brokerage approach may perform better than those using a light touch delivery model, such as providing advice through a website".
This fact seems indicate that the algorithm based on the Ungerboeck approach may perform better than the algorithm based on the Forney observation model (Figure 3).
The proposed method [Eq. (10)] is based on Kazemi dual-porosity dual-permeability model that has a layered formation, and therefore this approach may perform reasonably well in the formations with high density low angle fracture network, more specifically in the range of the fracture parameters used in sensitivity analysis.
The approach may perform better with controlled term or concept vocabularies, or could be further tested with Gene Ontology (GO) terms and other information in future studies.
Though our models were developed using PAH concentrations in visceral tissue, the approach may perform comparably for tissues more commonly consumed by the public.
Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties.
As a synthesis, both approaches appear as complementary, as STRUCTURE may perform slightly better in the systematic detection of admixed individuals, an advantage however balanced by a lower amount of misleading detection of hybrids (i.e. type I errors) obtained with network analysis.
Were Tophat to yield meaningful alignment quality scores, a statistical approach of the kind hinted at above may perform similarly to the k-mer based approach.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com