Li Hsu

Concordia detects spatial domains in population-level spatial proteomics data using a GNN-based framework, revealing a cancer-associated fibroblast subset linked to clinical outcomes in lung cancer.

CausalGRN infers causal gene regulatory networks from single-cell CRISPR screens and predicts responses to unseen perturbations with improved accuracy.

The paper uses a polygenic risk score for colorectal cancer in a PheWAS to find genetic correlations with other phenotypes from electronic health records.

The paper compares ancestry calibration methods for a colorectal cancer polygenic risk score using All of Us data, finding that using admixture and diverse training sets improves normality and tail accuracy.

The paper selects and validates ten polygenic risk scores for clinical use in diverse populations, focusing on chronic diseases and addressing disparities in predictive performance.

ROKET uses kernel regression and optimal transport to associate somatic mutations with clinical outcomes, identifying associations in three cancer types.

The paper investigates the impact of reference level choice on multinomial logistic regression random-effect score tests and proposes invariant procedures.

This study assesses polygenicity and risk prediction using common variants across 14 cancers, finding varying numbers of susceptibility variants and risk score effectiveness.

This study uses Mendelian randomization to show that higher physical activity levels are associated with lower risks of breast and colorectal cancer.

This paper improves polygenic risk scores by applying winners curse correction and variable thresholds to GWAS summary data, enhancing prediction accuracy for some diseases.