Paul A. Townsend

The paper improves prostate cancer risk stratification across multiple ancestries using a new polygenic hazard score with additional SNPs, showing higher hazard ratios and positive predictive value for clinically significant prostate cancer.

This study evaluates a polygenic hazard score in multi-ethnic populations and finds it is associated with age at prostate cancer diagnosis, aggressive diagnosis, and death across European, Asian, and African ancestries.

This study improves a polygenic hazard score for prostate cancer by adding 120 SNPs, enhancing hazard ratios but not positive predictive value of PSA testing.

This study integrates a large prostate cancer GWAS with gene expression data to identify 235 genes associated with risk, prioritizing causal genes through Bayesian credible sets.

The paper uses deep learning and association rule mining to predict drug response in cancer cell lines, outperforming existing methods and demonstrating a novel approach for therapeutic target identification.