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These classification is based on both severity for the disease and image properties.
(These classification scores are available at: http://www.ct.gov/doc/lib/doc/PDF/PDFReport/ClassificationManualLibraryCopy.pdf).pdf
These classification systems provide detailed stand-level characterization of forest ecosystems at a local level.
These classification models are useful for early diagnosis and understanding the mechanism of disease progression [66 69].
These classification models could be used as a virtual screening tool to predict the active class of new candidates.
These classification systems depend on agreement between clinicians who specialise in ALS to recognize underlying disease patterns, and as such are subjective.
These classification methods are commonly used for analysis of high-throughput molecular data [4], [19] [21] and have many attractive theoretical and empirical properties.
These classification results (Table 1; Figure S3) have been visualized as a topological map of the compartmentalized metabolome (Figure 7; for single analytes see Data S6).
These classification systems are fully compatible.
These classification tags are used for reporting by downstream applications.
These classification criteria were updated in 2010 with significant changes.
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