ACP-CapsPred: an explainable computational framework for identification and functional prediction of anticancer peptides based on capsule network.

ABSTRACT: Cancer is a severe illness that significantly threatens human life and health. Anticancer peptides (ACPs) represent a promising therapeutic strategy for combating cancer. In silico methods enable rapid and accurate identification of ACPs without extensive human and material resources. This study proposes a two-stage computational framework called ACP-CapsPred, which can accurately identify ACPs and characterize their functional activities across different cancer types. ACP-CapsPred integrates a protein language model with evolutionary information and physicochemical properties of peptides, constructing a comprehensive profile of peptides. ACP-CapsPred employs a next-generation neural network, specifically capsule networks, to construct predictive models. Experimental results demonstrate th

SUBMITTER: Yao L 

PROVIDER: S-EPMC11410379 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature