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In Figure 10b, SVD prefers mixed type datasets but not time series datasets.
Time series, non-time series and mixed type datasets were used as benchmark datasets, and the performance of each algorithm was evaluated using different measures mentioned above.
For mixed type datasets, we chose the datasets from Lymphoma [ 41] (focused on two experimental subsets corresponding to Blood B cells and Thymic T cells), Baldwin [ 42], Yoshimoto02 [ 43], Brauer05 [ 44] and Ronen05 [ 45].
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In Figure 8c, the performance is not much different between LLS-like algorithms and KNN-like algorithms in mixed type dataset against non-time series dataset.
Three types of microarray data (time series, non-time series and mixed type) were used as benchmark datasets, and the performance of each algorithm was evaluated using three kinds of measures (NRMSE, CPP and BLCI) and the average of these measures (called the average index).
In Figure 10a and 10b, ILLS is considered as the optimal algorithm, which can be used when the type of the dataset is either time series or mixed type.
Thus, KLE behaves as mixed type inhibitor.
Potentiodynamic polarisation studies show that extracts are mixed type inhibitors.
which is called a mixed type functional equation.
This indicates that this extract acts as mixed type inhibitors.
This inhibitor acts as a mixed type inhibitor.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com