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Ineffective chemotherapy is not only useless, but harmful in the neoadjuvant setting; prediction of chemotherapeutic response, which differ among patients and cancers, is therefore critical.
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We performed ENM using the Genetic Algorithm for Rule-set Prediction (GARP) to predict the optimal and potential ecological niche areas of C. gattii in BC.
The acceptable parameters of test set prediction, such as Rpred2= 0.768 and rm test)2="0.711 suggested that external predictivity of the model was significant.
The results are shown in Figure 5 where blue, red and green dots represent training set, test set and prediction set reactions, respectively.
To visualise connections between data sets prediction networks were created.
Outliers are shown as filled circles in the test set prediction and residuals plots.
Outliers are shown as filled circles in the test set prediction and residuals columns.
Distribution maps were generated for historically recorded species using genetic algorithm for rule-set prediction (GARP).
QSAR model have been evaluated internally and externally using test set prediction.
The validation of the model was done by cross validation, randomization and external test set prediction.
Figure 4 shows the different K 1 (the cross set prediction order) and error power values for 4 example clips.
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