Xiangjun Du

This paper introduces fluProfiler, a framework using viral sequences to predict antigenic relationships and improve vaccine strain selection by reducing reliance on time-consuming HI assays.

This paper introduces FluAdaX, a deep learning framework to predict influenza A virus cross-species transmission potential using genomic data, identifying key genes and variants associated with host adaptation.

This study models the antigenic evolution of seasonal influenza viruses using PREDAC and finds no need to update vaccine strains for the 2025 season.

DiffDomain identifies structurally reorganized TADs using Hi-C and single-cell Hi-C data, outperforming existing methods in accuracy and detecting new TAD subtypes.

The paper updates an epidemiological model to predict influenza incidence for the 2018-2019 season and evaluates its accuracy for the 2017-2018 season.

The paper demonstrates a method to forecast seasonal H3N2 influenza incidence by integrating evolutionary changes with an epidemiological model, achieving accurate real-time predictions for the 2016/2017 season.