Wei Zheng

The paper uses deconvolution to identify cell-type-specific DNA methylation and gene expression associations with colorectal cancer risk, finding 178 CpG sites and 68 genes across 26 new loci.

This paper develops a germline polygenic score (PRSagg) to predict prostate cancer aggressiveness and validates it across multiple datasets.

This study identifies novel genetic risk loci for pancreatic ductal adenocarcinoma in African ancestry individuals through admixture mapping and GWAS.

This study integrates large-scale omics and electronic health records to identify risk proteins and drugs for cancer prevention, finding 101 novel proteins and three drugs associated with reduced colorectal cancer risk.

The paper develops and validates multi-ancestry polygenic risk scores for LUAD in East Asian never-smokers, showing improved risk prediction compared to East Asian-only models.

The paper develops a polygenic risk model for prostate cancer using VA data and validates it in external cohorts, showing its clinical utility in risk stratification.

This study introduces transTF-TWAS, a method that integrates transcription factor-linked trans-located variants into transcriptome-wide association analyses to improve disease risk gene discovery.

The paper identifies 58 risk genes associated with common cancers by linking genetically predicted APA levels to cancer risk using GWAS data.

The paper applies transfer learning to adapt a general-purpose model to a specific domain using limited data, demonstrating improved performance in breast cancer-related tasks.

The paper identifies tissue-specific DNA methylation biomarkers for cancer risk by integrating genome and epigenome data from the GTEx consortium and GWAS data.

OncoPubMiner is a platform that mines oncology publications to create structured knowledge bases, enabling efficient and accurate gathering of precision oncology insights from scientific articles.

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 improves transcriptome-wide association studies by integrating pleiotropy-guided methods with GWAS data to identify new cancer susceptibility genes.

The paper assesses how the performance of an African-ancestry-specific polygenic hazard score varies with local ancestry in 8q24 using genetic data from 6,253 men with African ancestry.

The paper identifies novel genetic loci associated with blood pressure that interact with sleep duration across multiple ancestries.

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.

The paper uses a multi-layered approach combining GWAS, spatial autocorrelation, and Hi-C to identify novel genetic variants associated with pancreatic cancer susceptibility.

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.

The paper improves a polygenic hazard score for prostate cancer age at diagnosis by adding SNPs specific to African ancestry, enhancing performance in this population.

This study combines genome-wide association data from four cancers to identify shared and novel genetic associations and pleiotropic effects.

This paper identifies 56 new type 2 diabetes loci in 433,540 East Asian individuals through a meta-analysis of GWAS data.

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.