Foundations for improved vaccine correlate of risk analysis using positive-unlabeled learning.
Foundations for improved vaccine correlate of risk analysis using positive-unlabeled learning.
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DOI：
10.1080/21645515.2023.2204020
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发表时间：
2023-12-31
影响因子：
4.8
通讯作者：
中科院分区：
文献类型：
--
作者：
对疫苗效力田间试验提供的保护机制的深入了解可能因暴露率和保护率低而复杂化。然而，这些障碍并不妨碍发现感染风险降低（CoR）的相关因素，这是定义保护相关因素（CoP）的关键第一步。鉴于对大规模人类疫苗有效性试验的重大投资和收集的免疫原性数据以支持CoR发现，迫切需要分析有效性试验以最佳支持CoP发现的新方法。通过模拟免疫学数据和评估几种机器学习方法，这项研究为部署阳性/未标记（P/U）学习方法奠定了基础，这些方法旨在区分两组，其中只有一组具有明确的标签，而另一组仍然模糊。这一描述适用于疫苗效力田间试验的病例对照分析设计：感染受试者或病例根据定义是未受保护的，而未感染受试者或对照可能是受保护的或未受保护的，但只是从未暴露。在这里，我们研究了应用P/U学习的价值，使用基于预测保护状态的模型免疫原性数据对研究对象进行分类，以支持对疫苗介导的感染保护机制的新见解。我们证明了P/U学习方法可以可靠地推断保护状态，支持发现在感染状态病例和对照的传统比较中未观察到的模拟CoP，并且我们提出了实际部署这种新方法所需的后续步骤。
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 in cases where only one group has a definitive label and the other remains ambiguous. This description applies to case–control analysis designs for field trials of vaccine efficacy: infected subjects, or cases, are by definition unprotected, whereas uninfected subjects, or controls, may have been either protected or unprotected but simply never exposed. Here, we investigate the value of applying P/U learning to classify study subjects using model immunogenicity data based on predicted protection status in order to support new insights into mechanisms of vaccine-mediated protection from infection. We demonstrate that P/U learning methods can reliably infer protection status, supporting the discovery of simulated CoP that are not observed in conventional comparisons of infection status cases and controls, and we propose next steps necessary for the practical deployment of this novel approach to correlate discovery.
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影响因子：
4.5
作者：
Chicco D
通讯作者：
Chicco D
免疫途径定义了疫苗介导的对SIV的保护的多种机制。
影响因子：
82.9
作者：
Ackerman ME;Das J;Pittala S;Broge T;Linde C;Suscovich TJ;Brown EP;Bradley T;Natarajan H;Lin S;Sassic JK;O'Keefe S;Mehta N;Goodman D;Sips M;Weiner JA;Tomaras GD;Haynes BF;Lauffenburger DA;Bailey-Kellogg C;Roederer M;Alter G
通讯作者：
Alter G
影响因子：
7.5
作者：
Bekker, Jessa;Davis, Jesse
通讯作者：
Davis, Jesse
影响因子：
15.9
作者：
Neidich, Scott D.;Fong, Youyi;Novak, Richard
通讯作者：
Novak, Richard
影响因子：
3.7
作者：
Camargo, Guilherme;Bugatti, Pedro H.;Saito, Priscila T. M.
通讯作者：
Saito, Priscila T. M.