In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

Prostate cancer is a paradigm tumor model for heterogeneity in almost every sense. Its
clinical, spatial, and morphological heterogeneity divided by the high-level molecular …

Functional genomics data has the potential to increase GWAS power by identifying SNPs
that have a higher prior probability of association. Here, we introduce a method that …

Epigenetic processes govern prostate cancer (PCa) biology, as evidenced by the
dependency of PCa cells on the androgen receptor (AR), a prostate master transcription …

Poor trans-ancestry portability of polygenic risk scores is a consequence of Eurocentric
genetic studies and limited knowledge of shared causal variants. Leveraging regulatory …

Transcriptome-wide association studies using predicted expression have identified
thousands of genes whose locally regulated expression is associated with complex traits …

Although genome-wide association studies (GWAS) for prostate cancer (PrCa) have
identified more than 100 risk regions, most of the risk genes at these regions remain largely …

Quantifying the genetic correlation between cancers can provide important insights into the
mechanisms driving cancer etiology. Using genome-wide association study summary …

Artificial intelligence (AI) in oncology is advancing beyond algorithm development to
integration into clinical practice. This review describes the current state of the field, with a …

Abstract Disparities between African American and Caucasian men in prostate cancer (PCa)
diagnosis and treatment in the United States have been well established, with significant …