Prediction and analysis of anti-aging peptides using data augmentation and machine learning algorithms.

ABSTRACT:

Background

For most species, Aging is an inevitable biological process that poses significant challenges to global healthcare due to age-related diseases. Recent advances in peptide therapy have highlighted anti-aging peptides (AAPs) as a promising therapeutic strategy, owing to their low immunogenicity and ease of synthesis. However, the lack of computational tools for AAP identification has hindered systematic research in this field.

Results

In this study, we provided a benchmark dataset of anti-aging peptides (AAPs) based on their annotated biological functions and AgingBase database. Subsequently, we proposed three novel predictive models, including 1) Antiaging-FL, which integrates feature representation learning with machine learning algorithms; 2) ESM_GAN, which utili

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC12729081 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature