Robert John Tibshirani

CuGen is a GPU-accelerated framework for large-scale genomics that enables efficient GWAS and fine-mapping with robust confounding control.

This study investigates how cell cycle dynamics influence T cell fate and exhaustion by analyzing single-cell data and manipulating cell cycle progression and signaling.

The paper uses mass cytometry to analyze immune and signaling profiles in CML patients within hours of TKI treatment to predict 12-month treatment response.

The paper develops a method to identify genetic predictors of cardiometabolic disease progression using time-to-event data from the UK Biobank, finding ten polygenic hazard scores that significantly predict disease progression.

The paper uses spatial imaging and computational analysis to identify features in metastatic tumors that predict response to immune checkpoint inhibition in TNBC patients.

The paper develops sparse polygenic risk scores for 813 traits using UK Biobank data, showing significant predictive performance and a correlation between variant count and performance.

SparseSignatures is a framework for discovering mutational signatures in tumor genomes, outperforming existing methods and identifying signatures linked to known mutagenic mechanisms in pancreatic and breast tumors.

The paper develops efficient solvers for large-scale regularized regressions on genetic data, reducing memory usage and improving computational speed.

This paper introduces a latent factor model for polygenic risk scores that decomposes genetic risk into interpretable components, showing improved interpretability over standard PRS for traits like BMI and myocardial infarction.

This paper introduces a scalable framework for sparse regression on large-scale data, demonstrating improved prediction performance on UK Biobank traits using the BASIL method and snpnet package.

The paper introduces a group-regularized Cox regression method for survival analysis of rare events using common responses, showing improved prediction performance on UK Biobank data.

The paper develops a scalable sparse reduced-rank regression method for high-dimensional multitask learning, applied to UK Biobank data to generate polygenic risk scores.

The paper introduces a machine learning method that integrates immunological knowledge to improve predictive power from mass cytometry data.

The paper maps EMT and MET states in lung cancer using single-cell resolution and a novel computational tool, showing differences in trajectories and providing a framework for clinical sample characterization.

UK Biobank genetics of 38 blood and urine lab tests yields 1,857 independent loci; combining biomarker and disease polygenic scores improves FinnGen prediction of incident renal failure and alcoholic cirrhosis.