Foundations for improved vaccine correlate of risk analysis using positive-unlabeled learning.

ABSTRACT: Insights into mechanisms of protection afforded by vaccine efficacy field trials can be complicated by both low rates of exposure and protection. However, these barriers do not preclude the discovery of correlates of reduced risk (CoR) of infection, which are a critical first step in defining correlates of protection (CoP). Given the significant investment in large-scale human vaccine efficacy trials and immunogenicity data collected to support CoR discovery, novel approaches for analyzing efficacy trials to optimally support discovery of CoP are critically needed. By simulating immunological data and evaluating several machine learning approaches, this study lays the groundwork for deploying Positive/Unlabeled (P/U) learning methods, which are designed to differentiate between two groups

SUBMITTER: Kelkar NS 

PROVIDER: S-EPMC10294723 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature