MoCETSE Predicts Gram-Negative Bacterial Secreted Effectors
Researchers Shi et al. publish on March 11, 2026 in PLoS Computational Biology proposing MoCETSE, a deep learning model that predicts Gram-negative bacterial secreted effectors. It uses ESM-1b to extract contextual sequence embeddings, a target preprocessing network to refine key features, and a transformer with relative positional encoding to model residue relationships, demonstrating robust five-fold cross-validation and independent-test performance while enabling genome-wide effector prediction.
Scoring Rationale
Peer-reviewed, directly usable method with solid performance; novelty is moderate, focused on a specific Gram-negative effector prediction niche.
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Sources
- Read OriginalMoCETSE: A mixture-of-convolutional experts and transformer-based model for predicting Gram-negative bacterial secreted effectorsjournals.plos.org
