Brian L. Hill

The paper explores the relationship between genomic ancestry and health outcomes using a biobank linked to electronic health records, finding disease stratification across ancestry clusters.

This study evaluates methylation risk scores (MRS) in clinical datasets, showing they improve EHR imputation and predict phenotypes better than polygenic risk scores or EHR-based methods alone.

This paper introduces sensitivity analysis tools for Mendelian randomization to assess robustness against population stratification, batch effects, and horizontal pleiotropy.

BATMAN integrates single-cell RNA-Seq datasets efficiently, outperforming existing methods by up to 80% in batch effect correction while preserving cell relationships.

The paper develops a distributed platform for scalable genomic analysis in precision medicine, demonstrating linear scaling for storage, import, and query times.

This study benchmarks error-correction methods for next-generation sequencing data, finding significant variation in performance across datasets with no single method being optimal overall.

This paper develops a machine learning model using EMR data to predict in-hospital mortality preoperatively, achieving higher accuracy than existing clinical scores.

The paper assesses the installability and archival stability of computational biology tools, finding many are inaccessible or hard to install, and recommends policy changes to improve these aspects.