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Figure 2 | BMC Bioinformatics

Figure 2

From: A mixture model with a reference-based automatic selection of components for disease classification from protein and/or gene expression levels

Figure 2

model validation. Sensitivities, Figure 2a, and specificities, Figure 2b, (with standard deviations as error bars) estimated by linear SVM classifier and 100 independent two-fold cross-validations using two disease specific and two control specific components. Components were extracted from the linear mixture models based on control reference (c.r.) sample, model (2a), and disease reference (d.r.) sample, model (2b), where each sample was comprised of ten orthogonal components containing K= 15000 features. One component contained in prevailing concentration disease specific features, one control specific features and eight components contained features equally expressed in control and disease labelled samples. Relative concentration (expressed through a mixing angle) across the sample population has been: for disease specific features in the range of 500 to 89.990; for differentially not expressed features in the range of 250 to 650; and for control specific features in the range of 0.010 to 400. Assumed overall number of components has been M= 2 (red bars), M = 3 (green bars), M= 4 (blue bars) and M = 5 (magenta bars).

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