A deep learning model for predicting systemic lupus erythematosus-associated epitopes.
Journal:
BMC medical informatics and decision making
Published Date:
Jul 1, 2025
Abstract
BACKGROUND: The accurate prediction of epitopes associated with Systemic Lupus Erythematosus (SLE) plays a vital role in advancing our understanding of autoimmune pathogenesis and in designing effective immunotherapeutics. Traditional bioinformatics methods often struggle to capture the intricate sequence patterns and high-dimensional signals characteristic of epitope data. Deep learning presents a compelling alternative, with its ability to perform automatic feature learning and model complex dependencies inherent in biological sequences. This study proposes a hybrid deep learning architecture that synergistically integrates handcrafted biochemical features with data-driven deep sequence modeling to improve the identification of SLE-associated epitopes.
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