Figure 5
From: Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data
The results from applying the KIGP to one of the training sets of the simulated example 2, where (a) and (b) are for the simulation with an PK; (c) and (d) are for the simulation with an GK. (a) The estimated marginal posterior PMF of the degree parameter d. (b) The NLF plot of each gene for the simulation with the PK(2); the dots mark the prescribed significant genes. For this training set, all 10 preset significant genes and 1 false positive gene were found. (c) The estimated marginal posterior PDF of the width parameter r (solid line) versus its prior PDF (dotted line). The mode of the posterior PDF is at around 0.64. (d) The NLF plot for each gene for the simulation with the GK(0.64). The legends are same as those in (b). For this training set, all 10 preset significant genes were found with no false positive result.