A flexible class of latent variable models for the analysis of antibody response data

2025-12-16

AI Summary

Conventional antibody modeling relies on finite mixture models based on binary serostatus (negative/positive), failing to capture the continuous and age-dependent dynamics of immune responses. Method: We propose a continuous latent-variable framework that characterizes individual immune status along a continuum—from weak to strong—thereby abandoning the restrictive binary assumption. Our approach integrates Bayesian inference, mixed-effects structures, and joint age–response modeling, unifying mechanistic, regression-based, and mixture-model paradigms within a single coherent framework. Contribution/Results: Evaluated on malaria seroepidemiological data, the framework substantially improves cross-age-group fit accuracy for antibody distributions and more precisely reconstructs immunological maturation trajectories. Moreover, it is readily extensible to multi-omics quantitative analyses. By replacing discrete seroclassification with continuous immune-state estimation, this work introduces a paradigm shift in seroepidemiological modeling and analysis.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Bayesian Learning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Existing approaches to modelling antibody concentration data are mostly based on finite mixture models that rely on the assumption that individuals can be divided into two distinct groups: seronegative and seropositive. Here, we challenge this dichotomous modelling assumption and propose a latent variable modelling framework in which the immune status of each individual is represented along a continuum of latent seroreactivity, ranging from minimal to strong immune activation. This formulation provides greater flexibility in capturing age-related changes in antibody distributions while preserving the full information content of quantitative measurements. We show that the proposed class of models can accommodate a great variety of model formulations, both mechanistic and regression-based, and also includes finite mixture models as a special case. We demonstrate the advantages of this approach using malaria serology data and its ability to develop joint analyses across all ages that account for changes in transmission patterns. We conclude by outlining extensions of the proposed modelling framework and its relevance to other omics applications.

Problem

• Models antibody response as a continuum of latent seroreactivity
• Captures age-related changes in antibody distributions flexibly
• Enables joint analyses across ages accounting for transmission changes

Innovation

• Latent variable models represent immune status continuously
• Flexible framework captures age-related antibody distribution changes
• Accommodates mechanistic and regression-based model formulations