High-dimensional-omics data such as genomic, transcriptomic, and metabolomic data offer
great promise in advancing precision medicine. In particular, such data have enabled the …

This article discusses the fundamental principles of causal inference–the area of statistics
that estimates the effect of specific occurrences, treatments, interventions, and exposures on …

Motivation With the rapid development of modern technologies, massive data are available
for the systematic study of Alzheimer's disease (AD). Though many existing AD studies …

Recently, there has been increased interest in fusing multimodal imaging to better
understand brain organization by integrating information on both brain structure and …

The pathogenesis of Alzheimer disease (AD) involves complex gene regulatory changes
across different cell types. To help decipher this complexity, we introduce single-cell …

There has been an increasing interest in decomposing high-dimensional multi-omics data
into a product of low-rank and sparse matrices for the purpose of dimension reduction and …

There is a growing body of literature on knowledge-guided statistical learning methods for
analysis of structured high-dimensional data (such as genomic and transcriptomic data) that …

Selecting informative nodes over large-scale networks becomes increasingly important in
many research areas. Most existing methods focus on the local network structure and incur …

Identifying biomarkers for Alzheimer's disease with a goal of early detection is a fundamental
problem in clinical research. Both medical imaging and genetics have contributed …

In high-dimensional prediction problems, where the number of features may greatly exceed
the number of training instances, fully Bayesian approach with a sparsifying prior is known to …