1. GeM-LR: Discovering predictive biomarkers for small datasets in vaccine studies.
- Author
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Lin Lin, Rachel L Spreng, Kelly E Seaton, S Moses Dennison, Lindsay C Dahora, Daniel J Schuster, Sheetal Sawant, Peter B Gilbert, Youyi Fong, Neville Kisalu, Andrew J Pollard, Georgia D Tomaras, and Jia Li
- Subjects
Biology (General) ,QH301-705.5 - Abstract
Despite significant progress in vaccine research, the level of protection provided by vaccination can vary significantly across individuals. As a result, understanding immunologic variation across individuals in response to vaccination is important for developing next-generation efficacious vaccines. Accurate outcome prediction and identification of predictive biomarkers would represent a significant step towards this goal. Moreover, in early phase vaccine clinical trials, small datasets are prevalent, raising the need and challenge of building a robust and explainable prediction model that can reveal heterogeneity in small datasets. We propose a new model named Generative Mixture of Logistic Regression (GeM-LR), which combines characteristics of both a generative and a discriminative model. In addition, we propose a set of model selection strategies to enhance the robustness and interpretability of the model. GeM-LR extends a linear classifier to a non-linear classifier without losing interpretability and empowers the notion of predictive clustering for characterizing data heterogeneity in connection with the outcome variable. We demonstrate the strengths and utility of GeM-LR by applying it to data from several studies. GeM-LR achieves better prediction results than other popular methods while providing interpretations at different levels.
- Published
- 2024
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