Fig 3. Pre-filtering of learning datasets can reduce the accuracy of predictive models.

Shown is the predicted sensitivity of breast cancer cell lines to doxorubicin by two SVM models built using different learning datasets. In one case, the model was built using a learning dataset limited to the expression of 297 genes previously associated with cancer onset/progression [19]. In the other case, the model was built using a learning dataset drawn from all significantly expressed genes (Table A in S2 File). The results indicate that pre-filtering of the learning dataset to only include gene expression values of previously identified cancer related genes reduces predictive accuracy. (A) Quadrant plot of SVM predicted sensitivity to doxorubicin vs. observed sensitivity to doxorubicin of model built using a learning dataset pre-filtered for genes previously associated with cancer onset/progression; (B) Quadrant plot of SVM predicted sensitivity to doxorubicin vs. observed sensitivity to doxorubicin of model built using all gene expression data (Table A in S2 File); (C) ROC curves of the two models showing reduced predictive accuracy associated with the pre-filtered learning dataset (Red circles = drug sensitive training set; Blue circles = drug resistant training set; Black diamonds = breast cancer cells test set).