Binglan Li

This paper introduces PhenoPLIER, a method that maps gene-trait and drug associations into gene modules to reveal disease mechanisms and drug targets.

The paper evaluates the frequency and impact of pharmacogenetic alleles in a diverse biobank population, finding high prevalence of actionable alleles and disparities by ancestry.

This paper introduces polygenic risk vectors (PRV) to improve genetic risk stratification for cardio-metabolic diseases by combining additive and dominant genetic effects, outperforming traditional polygenic risk scores.

The paper uses multi-ancestry biobank data and integrative methods to identify gene-disease associations across different ancestry groups, finding shared and ancestry-specific genetic links.

The paper introduces TSA-TWAS, a framework that improves TWAS power by considering tissue specificity, identifying novel gene-trait associations in HIV-positive adults.

The paper introduces a collective feature selection approach to identify epistatic variants by combining multiple methods, showing improved performance in detecting true positive interactions in both simulated and real BMI data.