Abstract
The rapid growth of biological datasets presents an opportunity to leverage past studies to inform and predict outcomes in new experiments. A central challenge is to distinguish which serological patterns are universally conserved and which are specific to individual datasets. In the context of human serology studies, where antibody-virus interactions assess the strength and breadth of the antibody response and inform vaccine strain selection, differences in cohort demographics or experimental design can markedly impact responses, yet few methods can translate these differences into the value±uncertainty of future measurements. Here, we introduce CAPYBARA, a data-driven framework that quantifies how serological relations map across datasets. As a case study, we applied CAPYBARA to 25 influenza datasets from 1997-2023 that measured vaccine or infection responses against multiple influenza variants using hemagglutination inhibition (HAI). To demonstrate how a subset of measurements in each study can infer the remaining data, we withheld all HAI measurements for each variant and accurately predicted them with a 2.0-fold mean absolute error-on par with experimental assay variability. Although studies with similar designs showed the best predictive power (e.g., children data are better predicted by children than adult data), predictions across age groups, between vaccination and infection studies, and across studies conducted <10 years apart showed comparable 2‒3-fold accuracy. By analyzing feature importance in this interpretable model, we identified global cross-reactivity trends that can be directly applied in future longitudinal or vaccine studies to infer broad serological responses from a small subset of measurements.