Generative models are increasingly used to model adaptive immune receptor repertoire (AIRR) sequence distributions, promising to decode the sequence diversity shaping immune responses and accelerate the design of therapeutic antibodies and T-cell receptors. Yet it remains unclear whether these models produce biologically meaningful outputs or merely capture surface-level sequence statistics while missing features driven by receptor generation and selection. Rigorous evaluation is needed, but the field lacks established standards, as existing machine learning metrics do not all translate directly to the AIRR domain, given the complex structure of the data and the lack of biological ground truth. Consequently, researchers face difficulties in evaluating the models and selecting appropriate ones, which can critically affect downstream clinical applications. Here, we apply a suite of evaluation metrics tailored to AIRR sequence data and present a systematic comparison of popular generative model families proposed for the AIRR field, including variational autoencoders, long short-term memory networks, antibody language models, selection models, and simple statistical baselines. We focus specifically on the task of learning individual-specific immune receptor repertoires, a clinically relevant challenge with direct implications for personalized immunotherapy, disease monitoring, and vaccine response studies. By analyzing the sequences generated by each model, we identify memorization risks, innovation capabilities, and sensitivity to hyperparameter tuning. Taken together, these results advance the understanding of how current generative models reproduce the biology of individual immune repertoires and lay the groundwork for more principled model development and evaluation.
Charlotte Würtzen, Maria Mamica, C. Kanduri et al.· bioRxiv· 0 citations
Systems vaccinology approaches have identified factors affecting vaccine responses in multiple studies, but the ability of computational models to generalize these findings to unseen data remains unclear. We established a community resource to create and compare models predicting B. pertussis booster vaccination responses and put such modeling approaches to the test. We compiled multi-modal experimental training data from three independent cohorts (n=117 individuals), and asked investigators to predict vaccine responses in a cohort of 54 newly recruited individuals using only their pre-booster vaccination data. We benchmarked a total of 107 computational models. Top-performing models were characterized by workflows that prioritized rigorous data preprocessing, robust imputation of missing data, and the use of multi-omics integration or non-linear machine learning. We identified pre-existing antigen-specific antibody titers and baseline monocyte frequencies as the most consistent predictors of post-vaccination immunity, highlighting the dominant role of individual immune setpoints. We established the resulting datasets and evaluation framework as a community resource to advance predictive immunology and facilitate personalized vaccination strategies.
Pramod Shinde, Lisa Willemsen, Jiyeun Lee et al.· bioRxiv· 0 citations
Cancer epitopes, the molecular structures recognized by T and B cells at the tumor interface, are central to understanding antitumor immunity and developing immunotherapies. Yet despite the rapid growth of cancer immunology data, a comprehensive, continuously updated, and accessible resource for cancer epitope data has been lacking. The Cancer Epitope Database and Analysis Resource (CEDAR, cedar.iedb.org) was established in 2021 to fill this gap, providing curated experimental epitope data alongside a suite of cancer-specific computational tools for epitope prediction and analysis. Built on the validated infrastructure of the Immune Epitope Database (IEDB), CEDAR integrates cancer epitope data with biological, immunological, and clinical context, enabling researchers to explore immune recognition of tumors, identify candidate targets for immunotherapy, and benchmark prediction methods. Here we describe CEDAR’s current capabilities, report on progress in curation, database development, and tool availability, and outline the opportunities and challenges ahead for expanding its scope and utility to the cancer research community.
Zeynep Koşaloğlu-Yalçın, Ibel Carri, Daniel Marrama et al.· Frontiers in Oncology· 0 citations
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