Figure 2.

Performance comparison among MTGNN, NeuralNetworks, RandomForest, and Auto-Sklearn in constructing off-target models. (A) A bar chart compares the number of tasks (y-axis) corresponding to the maximum scores achieved by each method on AUROC, MCC, BACC, and F1 metrics. The number above each bar indicates the tasks with the highest score for that method. (B) Average performance, measured by BACC, is depicted for the seven types of target models under four off-target prediction models. (C) The performance of MTGNN and Neural Networks in tasks with different data volumes. The bar chart shows the average BACC (y-axis) for tasks with corresponding data volumes (x-axis). ManneWhitney U test is used to test for significant differences, where: ns indicates no significant difference; 0.01<∗P < 0.05; 0.001<∗∗P < 0.01. (D) The histogram of the number of human target tasks (y-axis) corresponding to different interval ranges (x-axis) of BACC. (E) Scatter plots depict Recall and Precision values for human target tasks. Each color represents a different target type, and dot size corresponds to the amount of available data for that target, with larger dots indicating larger datasets. The y-axis represents the positive rate of the overall data volume, while the x-axis represents the respective indicator value.