6 papers
AI-driven Multimodal Representation Learning for Latent Mediation Structure Discovery of Socioeconomic Disadvantage, Psychosocial Factors, and Cardiometabolic Multimorbidity: Insights from the All of Us Research Program
Cong Cao, Shuangge Ma
Social disadvantage is associated with multimorbidity, but the pathways linking social conditions to disease burden remain poorly understood. We developed an AI-driven multimodal m…
Sparse Bayesian Deep Functional Learning with Structured Region Selection
Xiaoxian Zhu, Yingmeng Li, Shuangge Ma +1
In modern applications such as ECG monitoring, neuroimaging, wearable sensing, and industrial equipment diagnostics, complex and continuously structured data are ubiquitous, presen…
Supervised Bayesian joint graphical model for simultaneous network estimation and subgroup identification
Xing Qin, Xu Liu, Shuangge Ma +1
Heterogeneity is a fundamental characteristic of cancer. To accommodate heterogeneity, subgroup identification has been extensively studied and broadly categorized into unsupervise…
Heterogeneous gene network estimation for single-cell transcriptomic data via a joint regularized deep neural network
Jingyuan Yang, Tao Li, Tianyi Wang +2
Estimation of intracellular gene networks has been a critical component of single-cell transcriptomic data analysis, which can provide crucial insights into the complex interplay b…
A flexible Bayesian framework for detecting cross-sample spatial expression variability in heterogeneous tissues
Meng Zhou, Shuangge Ma, Mengyun Wu
Spatial transcriptomics measures gene expression alongside the spatial coordinates of each capture spot or cell across tissue samples. The detection of spatially variable (SV) gene…
Joint identification of spatially variable genes via a network-assisted Bayesian regularization approach
Mingcong Wu, Yang Li, Shuangge Ma +1
Identifying genes that display spatial patterns is critical to investigating expression interactions within a spatial context and further dissecting biological understanding of com…