Knowledge-driven multimodal mutual learning for cell line-targeted anticancer peptide prediction.
A hybrid dataset compiled from five well-established peptide databases is integrated and proposed, and the effectiveness of TargetPC is demonstrated, which outperforms the state-of-the-art baselines in terms of prediction accuracy, and maintains strong generalization to unseen ACPs and cell lines.