Fig. 2: Overall performance of the SpHN-VDA in VDA prediction.

a The AUC for VDA prediction on the HDVD dataset across 7 independent experiments (each with a different random seed) for various negative sample proportions under positive-to-negative ratios of 1:1, 1:2, 1:5, and 1:10. For each ratio, 7 distinct sets of negative samples were randomly selected. Error bars represent the mean standard deviation across the 7 independent experiments, which differs from technical replicates. Each bar graph shows the performance significance of intergroup differences. The estimated effect sizes of four ratios are 0.94, 0.79, 0.86, and 0.87, respectively. The significance of SpHN-VDA versus DTINet is shown in each case (Tukey’s HSD test: *P = 2.65 × 10^-2 for 1:1, *P = 3.84 × 10^-2 for 1:2, ****P < 1 × 10^-4 for 1:5, and ****P < 1 × 10^-4 for 1:10). The details of statistical test result are reported in the Supplementary Table 11–14 and the significance test results based on the t-test are reported in the Supplementary Tables 8–10. b The AUC for VDA prediction on the VDA2 dataset with 7 independent experiments (each with a different random seed) for various negative sample proportions under positive-to-negative ratios of 1:1, 1:2, 1:5, and 1:10. For each ratio, 7 distinct sets of negative samples were randomly selected. Error bars represent the mean standard deviation across the 7 independent experiments, which differs from technical replicates. The estimated effect sizes of four ratios are 0.96, 0.98, 0.99, and 0.99, respectively. The significance of SpHN-VDA versus GAEMDA is shown in each case (Tukey’s HSD test: ****P <  1 × 10^-4 for 1:1, *P = 1.01 × 10^-2 for 1:2, *P = 1.88 × 10^-2 for 1:5, and ^nsP = 0.99 for 1:10). The details of statistical test result are reported in the Supplementary Tables 15–18 and the significance test results based on the t-test are reported in the Supplementary Tables 8–10. c The change in AUPR on HDVD and VDA2 datasets is illustrated under different negative proportions with 7 independent experiments (each with a different random seed). For each proportion, 7 distinct sets of negative samples were randomly selected, which differs from technical replicates. For boxplots, the center line represents the median, upper and lower edges represent the interquartile range, and the whiskers extend from the minimum to the maximum values. d Generalization evaluation for OOD scenarios portrays the distributions of AUC and AUPR under 9 independent experiments (each with a different random seed) regarding 20% of viruses as cold-start viruses. For each independent experiment, a distinct set of cold-start viruses were randomly sampled. The evaluation metrics are represented as violin plots, where the center line depicts the median and the upper and lower lines denote the interquartile range. e Under 9 independent experiments (each with a different random seed), robustness evaluation showing the best AUC of each model prediction against different ratios of random perturbation of VDA pairs where the pairs are replaced with adding or removing. For each perturbation ratio, 9 distinct sets of cold-start viruses and perturbative samples were randomly sampled, which differs from technical replicates. For boxplots, the center line represents the median, upper and lower edges represent the interquartile range, and the whiskers extend from the minimum to the maximum values. Source data are provided as a Source Data file in Supplementary Data 4.