TXSelect: A multi-task learning model to identify secretory effectors
Fig 4
Overview of the TXSelect framework for multi-task identification of secretory effectors.
(A) Dataset construction. Secretory effectors (T1SE, T2SE, T3SE, T4SE, and T6SE) were collected from the literature and redundancy was removed using CD-HIT. For each task, the target label was set to 1, whereas the labels of the other tasks were set to 0 (e.g., in the T1SE task, the T1SE label is 1, while the labels for T2/3/4/6SE are 0). (B) Model architecture. Multiple feature descriptors, including evolutionary scale modelling. (ESM) N-terminal mean embedding, distance-based residue (DR), and split amino acid composition (SC-PseAAC), were integrated to construct sequence representations. These representations are processed through a shared backbone network composed of stacked linear, ReLU, and dropout layers, followed by task-specific heads for predicting different effector types. (C) Model performance. Training loss and validation F1-scores of T1SE, T2SE, T3SE, T4SE, and T6SE tasks across 500 epochs. The curves demonstrate stable convergence of the shared multi-task framework and balanced performance across effector classes.