Alzheimer's Disease Classifier
Recent studies suggest that adaptive immune repertoires may contain disease-specific signatures, yet their application to neurodegenerative disorders remains underexplored. Here, we investigate whether antibody repertoires, encoded by antibody-specific protein language models (PLMs), can serve as classifiers for Alzheimer's disease (AD). We first reproduced AntiBERTa, a transformer-based PLM trained on antibody sequences, and developed multiple variants to capture repertoire-level representations. Encodings derived from IgG and IgA heavy-chain sequences, with or without clone-abundance weighting, were used to train models to distinguish AD patients from cognitively normal controls. To enhance performance, we incorporated one-dimensional convolutional neural networks and supervised contrastive learning, enabling the extraction of more discriminative sequence features. Our best model achieved an F1 score of 0.833 in internal cross-validation. External validation on independent control cohorts further demonstrated robust generalizability, yielding an F1 score of 0.990 when using a COVID-19 cohort as the negative control and 0.728 when using a general population cohort. These findings establish the feasibility of using antibody repertoire encodings for AD classification and underscore the potential of deep learning approaches tailored to immunosequencing data in neurodegenerative disease diagnostics.