ACP-GCN: The Identification of Anticancer Peptides Based on Graph Convolution Networks

ACP-GCN: The Identification of Anticancer Peptides Based on Graph Convolution Networks

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Anticancer peptide (ACP) is a class of anti-cancer peptide which can inhibit and kill tumor cells.Identification of ACPs is of great significance for the development of new anti-cancer drugs.However, most of computational methods make predictions based on machine learning using hand-crafted features.In this article, we propose a new graph learning based computational model, named ACP-GCN, to tokidoki hello kitty blind box automatically and accurately predict ACPs based on graph convolution networks.In this model, we for the first time take the ACP prediction as a graph classification task, where each peptide sample serra avatar price is represented as a graph.

The experimental results show that the proposed model outperforms most of state-of-the-art methods, demonstrating that the proposed method can effectively distinguish ACPs from non-ACPs.The excellent predictive ability will rapidly push forward their applications in cancer therapy.