6 citations · 6 across the 4 of their papers we have counts for
7 papers
Mathematical AI Predicts 40% and 57% Influenza Vaccine Effectiveness Against H1N1 and H3N2, Respectively, for the 2026--2027 U.S. Season
Xiang Xiang Wang, Guo-Wei Wei
Background: Clinical influenza vaccine effectiveness (VE) has often been low over the past two decades, highlighting the need for preseason VE forecasting. Timely forecasts could h…
GrassTop: Grassmannian k-mer Topology for Viral Classification and Phylogenetic Analysis
Xiang Xiang Wang, Guo-Wei Wei
We introduce GrassTop, a genome representation that integrates Grassmann manifolds and algebraic topology for viral classification and phylogenetic analysis. The framework begins b…
Data-Adaptive Grassmann Manifold Representations for Spatial Transcriptomics Alignment
Xiang Xiang Wang, Sean Cottrell, Guo-Wei Wei
Spatial transcriptomics measures gene expression together with spatial coordinates, but many existing analysis methods represent each spot primarily by a single feature vector. We…
Subspace Tensor Orthogonal Rotation Model (STORM) for Batch Alignment, Cell Type Deconvolution, and Gene Imputation in Spatial Transcriptomic Data
Sean Cottrell, Guo-Wei Wei, Longxiu Huang
Spatial transcriptomics data analysis integrates cellular transcriptional activity with spatial coordinates to identify spatial domains, infer cell-type dynamics, and characterize…
Multiscale Grassmann Manifolds for Single-Cell Data Analysis
Xiang Xiang Wang, Sean Cottrell, Guo-Wei Wei
Single-cell data analysis seeks to characterize cellular heterogeneity based on high-dimensional gene expression profiles. Conventional approaches represent each cell as a vector i…
Computational Drug Repurposing for Alzheimer's Disease via Sheaf Theoretic Population-Scale Analysis of snRNA-seq Data
Sean Cottrell, Seungmin Yoon, Xiaoqi Wei +2
Single-cell and single-nucleus RNA sequencing (scRNA-seq /snRNA-seq) are widely used to reveal heterogeneity in cells, showing a growing potential for precision and personalized me…
Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou, Tolga Birdal, Michael Bronstein +19
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new fron…