Figure 6.

Optimalisation of the number of clusters in hierarchical metamodelling of the mammalian circadian clock model. A) Results from inverse hierarchical metamodelling using from 1–20 clusters in the N-way HC-PLSR. Left: Mean parameter prediction correlation coefficient (R2)-values within the calibration set, over the nine varied circadian clock model input parameters vs. the number of clusters used in the N-way HC-PLSR metamodelling. The calibration set observations were here treated as "new observations" (see Figure 2), and classified in the prediction stage. Using six clusters was considered optimal. Right: Parameter prediction R2-values within the calibration set for the nine different circadian clock model input parameters vs. the number of clusters used in the N-way HC-PLSR metamodelling. B) Results from classical hierarchical metamodelling using from 1–20 clusters in the N-way HC-PLSR. Left: Mean state variable prediction R2-values within the calibration set, over the 16 circadian clock model state variables vs. the number of clusters used in the N-way HC-PLSR metamodelling. The calibration set observations were here treated as "new observations", and classified in the prediction stage. Right: State variable prediction R2-values within the calibration set for the 16 circadian clock state variables vs. the number of clusters used in the N-way HC-PLSR metamodelling. Using six clusters was considered optimal.

Tøndel et al. BMC Systems Biology 2012 6:88   doi:10.1186/1752-0509-6-88
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