MA-PEP: A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism.

ABSTRACT: AntiCancer Peptides (ACPs) have emerged as promising therapeutic agents for cancer treatment. The time-consuming and costly nature of wet-lab discriminatory methods has spurred the development of various machine learning and deep learning-based ACP classification methods. Nonetheless, current methods encountered challenges in efficiently integrating features from various peptide modalities, thereby limiting a more comprehensive understanding of ACPs and further restricting the improvement of prediction model performance. In this study, we introduce a novel ACP prediction method, MA-PEP, which leverages multiple attention mechanisms for feature enhancement and fusion to improve ACP prediction. By integrating the enhanced molecular-level chemical features and sequence information of peptides

SUBMITTER: Liang X 

PROVIDER: S-EPMC10966354 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature