After training on these noisy datasets via the cross-validation, the models with the best performance (highest F1-score) on the internal validation dataset are also assessed on the test set, which is not transformed by any random shuffling for the labels. According to Figure S13, the F1-score and MCC of the models (blue spheres) in the Y-randomization test is strikingly decreased relative to the original models (red spheres), which suggests that our original models are quite reliable. Meanwhile, the MCC of all these models in the Y-randomization test are close to 0, indicating that these models in the Y-randomization test have no better than the random prediction. Hence the Y-randomization test substantiates the reliability of the original models.