Figure 5.

Receiver Operating Characteristic (ROC) curves comparing the prediction performance of MENTHU and inDelphi to that of the novel MENTHU-based tools Moon Rover and Moon Walker. Moon Walker and Moon Rover are two machine-learning-based tools that utilize the same two features for PreMA predictions that MENTHU uses: the MENTHU Score and the distance between the microhomologies used for most expected MMEJ repair outcome. The ROC curves displayed represent the PreMA prediction performance of MENTHU, inDelphi, Moon Rover, and Moon Walker on the out-of-sample validation set described on Figure 2E. Here, sensitivity is plotted against 1 – specificity (or the probability of a type I error: α) as a function of varying prediction thresholds. See Figure 3 legend for explanation on MENTHU and inDelphi thresholds. The inset is a blowup of the region where MENTHU is present. The area under the curve for inDelphi, Moon Rover, and Moon Walker are 0.918, 0.916 and 0.916, respectively.