Search for: All records

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. 

Abstract The advent of 2D materials has revolutionized condensed matter physics and materials science, offering unprecedented opportunities to explore exotic physical phenomena, engineer novel functionalities, and address critical technological challenges across diverse fields. Over the past two decades, the exploration of 2D materials has expanded beyond graphene, encompassing a vast library of atomically thin crystals and their heterostructures. These materials exhibit extraordinary electronic, optical, thermal, mechanical, and chemical properties, and hold promise for breakthroughs in electronics, optoelectronics, quantum technologies, energy storage, catalysis, thermal management, filtration and separation, and beyond. Many exciting new physics and phenomena continue to emerge, while select 2D materials, such as graphene, h-BN, and the semiconducting transition metal dichalcogenides (TMDCs), are transitioning from laboratory-scale demonstrations to industrial applications. In this context, a holistic understanding of synthesis, structure-property relationships, integration, and performance optimization is essential. This roadmap reviews the multifaceted challenges and opportunities in 2D materials research, focusing on the synthesis, properties and applications of representative systems including graphene and its derivatives, TMDCs, MXenes as well as their heterostructures and moiré systems.