Predicting antigenic peptides using a multi-level pooling-based transformer model with enhanced Kolaskar & Tongaonkar's algorithm for feature selection.

Antigenic peptide (AP) prediction is one of the most important roles in improve vaccine design and interpreting immune responses. This paper develops a Multi-Level Pooling-based Transformer (MLPT) model, which improves the accuracy and efficiency of predicting T-cell epitopes (TCEs). The model has utilized peptide sequences from the Immune Epitope Database (IEDB) and utilized a refined Kolaskar & Tongaonkar algorithm for feature extraction as well as a Self-Improved Black-winged Kite optimization algorithm to optimize the scoring matrix.