TXSelect: A multi-task learning model to identify secretory effectors
Fig 3
Performance comparison of feature combinations and detailed performance of the optimal TXSelect framework.
(A–C) Model performance based on combinations of selected ESM pooling and classical sequence descriptors. Fusion experiments were conducted by combining ESM core region mean, ESM mean, and ESM N-terminal mean with DR, SC-PseAAC, and QSOrder. Bars indicate classification metrics (AUC, F1, Precision, Recall). Among these, ESM N-terminal mean + DR + SC-PseAAC achieved the highest validation F1 score (0.867) and also performed best on the test set (F1 = 0.8645). Adding QSOrder resulted in a comparable validation F1 score (0.863); however, its test performance decreased (F1 = 0.8507), confirming ESM N-terminal mean + DR + SC-PseAAC as the optimal combination. (D–E) Classification performance of the optimal TXSelect. Heatmaps show per-class performance of the optimal feature combination (ESM N-terminal mean + DR + SC-PseAAC) on the (D) validation dataset and (E) test dataset. Metrics (AUC, F1, Precision, Recall) are reported for each effector type (T1SE, T2SE, T3SE, T4SE, T6SE).