We developed ResLysEmbed, a ResNet+MLP-based hybrid model that integrates word and protein language model embeddings to predict lysine succinylation sites with superior accuracy and interpretability. It outperforms existing methods and provides biologically relevant insights through SHAP analysis. Supervised by
Prof Dr. Mohammad Saifur Rahman. Our work later got acccepted in Bioinformatics Advances.