Figure 4
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 for the non-linear case in the simulated example 1, where (a) and (b) are for the simulation with an LK; (c) and (d) are for the simulation with an GK; (e) and (f) for the simulation with an PK. All the legends are same as those in Fig. 3. (a) The NLF plot of each gene for the simulation with an LK; with the cutoff value for NLF (dotted line), none of the true preset significant genes was found (2 false negatives). Three false positive genes were misclassified as significant. (b) The contours of the posterior predictive probability of the class "1" for the simulation with an LK (given the two true preset significant genes). For this set of training samples, the testing MR is 0.5 (the Bayesian bound is 0.055). (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 0.063. (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 0.060.