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Figure 2 | BMC Bioinformatics

Figure 2

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

Figure 2

The results from applying the KIGP with an GK to one of the training sets of the simulated example 1, where (a) and (b) are for the linear case; (c) and (d) are for the non-liner case. (a) The estimated marginal posterior PDF of the width parameter of the GK (solid line) versus its prior PDF (dotted line). The mode of the posterior PDF is at around 1.61. (b) The local fdr with the GK(1.61) (with the standard normal as the density of NLF under null hypothesis); the horizontal dotted line represents the threshold of the fdr (0.05); the vertical dotted line shows the resulted cutoff value for NLF (3.83). (c) The estimated marginal posterior PDF of the width parameter of the GK (solid line) versus its prior PDF (dotted line). The mode of the posterior PDF is at around 0.81. (d) The local fdr with the GK(0.81) (with standard normal as the density of NLF under null hypothesis); the horizontal dotted line represents the threshold of the fdr (0.05); the vertical dotted line shows the resulted cutoff value for NLF (3.68).

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