To assess whether the PU learning strategy we proposed can identify potential positive samples from unlabeled samples, thereby improving the model's robustness, we conducted a recovery experiment of hidden positive samples. To this end, 1%, 3%, 5%, 10%, and 20% of the positive samples were mislabeled as negative, and the recognition capability of PU-EPP and a baseline model (models trained by conventional procedure) on these mislabeled samples was tested by five-fold cross-validation. Through the PU learning strategy, PU-EPP identified ~80% of mislabeled positive samples in the training process, while the baseline model trained through the conventional procedure (non-PU) only identified between ~40% to 65% (Fig 3C ). When 20% of the positive samples were mislabeled, PU-EPP still had a strong recognition capability, demonstrating its high robustness. We also evaluated the performance of the PU-EPP and the baseline model under each condition and found that PU-EPP also achieved higher AUC scores than the baseline model (Fig ). Thus, when there are biases and noise in the training datasets, the PU learning strategy employed herein could be considered an effective approach to improve the model's performance and robustness.