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Figure 3 | BMC Bioinformatics

Figure 3

From: Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

Figure 3

The results from applying the KIGP to one of the training sets of the linear case in the simulated example 1, where (a) and (b) are for the simulation with an LK; (c) and (d) are for the one with an GK; (e) and (f) for the one with an PK. (a) The NLF plot of each gene for the simulation with an LK; with the cutoff value for NLF (dotted line), two genes were found significant (the circles mark the preset significant genes). (b) The contours of the posterior predictive probability of the class "1" for the simulation with an LK, where X-axis is for the value of the gene 23 and Y-axis represents the value of the gene 57; the numbers associated with contours are the probabilities; the asterisks denote the training samples from the class "1"; the circles demonstrate the training samples from the class "-1"; the dotted line shows the Bayesian classifier. For this set of training samples, the testing MR is 0.022 (the Bayesian bound for MR is 0.013). (c) Same as (a) except it is for the simulation with an GK. (d) Same as (b) except it is for the simulation with an GK. The testing MR is 0.028. (e) Same as (a) except it is for the simulation with an PK. (f) Same as (b) except it is for the simulation with an PK. The testing MR is 0.017.

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