TXSelect: A multi-task learning model to identify secretory effectors
Fig 2
Classical sequence descriptor group ranking and performance of selected descriptors in multi-task classification.
(A–C) Feature group ranking based on silhouette scores. Supervised UMAP with 5-fold cross-validation was applied to evaluate the clustering ability of various handcrafted sequence descriptors. Bars indicate the mean validation silhouette score ± standard deviation for (A) TXSE (T1/2/3/4/6SE), (B) T1/2SE subset, and (C) T3/4/6SE subset. (D–E) Performance of selected descriptors. Radar plots summarize the classification performance of representative descriptors (DR, SC-PseAAC, PC-PseAAC, QSOrder, AAC, and APAAC) across tasks. Results are shown for the (D) validation set and (E) test set. Among these, DR, SC-PseAAC, and QSOrder consistently achieved strong performance across tasks.