Contrastive learning for enhancing feature extraction in anticancer peptides.

ABSTRACT: Cancer, recognized as a primary cause of death worldwide, has profound health implications and incurs a substantial social burden. Numerous efforts have been made to develop cancer treatments, among which anticancer peptides (ACPs) are garnering recognition for their potential applications. While ACP screening is time-consuming and costly, in silico prediction tools provide a way to overcome these challenges. Herein, we present a deep learning model designed to screen ACPs using peptide sequences only. A contrastive learning technique was applied to enhance model performance, yielding better results than a model trained solely on binary classification loss. Furthermore, two independent encoders were employed as a replacement for data augmentation, a technique commonly used in contrastive l

SUBMITTER: Lee B 

PROVIDER: S-EPMC11082072 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature