Figure 5

Overall model performances, including YPD essential genes. Predictive performance of the iFF708 (red), iLL672 (blue) and iND750 (green) models are shown for the 16 different conditions present in the current data set. For the calculations of the different metrics, true positive (TP) predictions were regarded as experimentally viable genes predicted to be viable, false positives (FP) as experimentally essential genes predicted to be viable, true negatives (TN) as experimentally essential genes predicted to be essential, and false negatives (FN) as experimentally viable genes predicted to be essential. Calculations of (a) sensitivity (TP/(TP + FN)), (b) specificity (TN/(TN + FP)), (c) negative predictive value (TN/(TN + FN)) and (d) correct rate ((TP + TN)/(TP + TN + FP + FN)) were done with genes essential under YPD considered to be essential under all conditions. Assessing models using a variety of metrics reveals that the models differ in their abilities to identify viable and unviable mutants. For example, the higher specificity of the iLL672 model under all conditions indicates that it identifies the largest proportion of essential genes. On the other hand, the higher negative predictive value of the iFF708 and iND750 models demonstrates that the percentage of correct essential predictions is lowest using the iLL672 model. This trade-off suggests that different models may be preferable for use in different applications, depending on the relative impact of false positives and false negatives.