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Yu et al. [19] created a bioscience image taxonomy (consisting of Gel-Image, Graph, Image-of-Thing, Mix, Model, and Table) and used Support Vector Machines to classify the figures, using properties of both the textual captions and the images.
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Using Equations (8) and (9), let us classify the images (Figure 8). Figure 8 Classification of route 1 fragments (with the threshold criterion ( 8) 0, 8 ≤ χ ≤ 1).
Principal components analysis (PCA) of nucleotide word frequencies (1 5) derived from the assembled libraries from each site was used to cluster and classify the metagenome assemblies (Figure 3).
CDPs in this figure classify the compound data sets considering molecular scaffolds, fingerprint representations, and physicochemical properties.
Parameters optimal for classifying one type of gene family yielded poor performance when used for classifying the other (Figure 3).
As shown in Figure 1, we classify the RTOs into two main types.
As shown in Figure 1, we classify the yeast proteins into 5 age classes based on taxonomy [ 46].
But it took years of analysis to figure out how to classify the surprising-looking creature.
In Figure 1, the classifier classifies the mixed traffic at Layer 2 data buffer according to the type of traffic.
In classifying the upregulated genes (Figure 1), such data for certain time points for some genes were also classified as upregulated time points.
The performance of both programs deteriorates if we use parameters tuned for one data set for classifying the respective other (Figure 3).
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