Edwin Wang

The paper evaluates somatic contamination in germline genomes using WES and WGS data from UK Biobank, TCGA, and 1000 Genomes Project, revealing limitations in current filtering methods and the impact of somatic mutations on genetic studies.

This paper introduces a computational framework, GSGP, to decipher somatic mutational signatures and identifies novel aetiologies, including an RNA-derived signature in colorectal cancer.

The paper introduces a self-attention graph convolutional network to identify key DNA methylation sites for multi-tumor early diagnostics, achieving improved sensitivity in blood-based detection.

eTumorMetastasis uses whole-exome sequencing data to predict tumor recurrence by analyzing network-based gene signatures from founding clones.

This study identifies seven germline genomic patterns associated with cancer risk, oncogenic pathways, and clinical outcomes across 22 cancer types.

The paper identifies inherited defects in NK cells, antigen processing, and Wnt pathways that shape tumor immune microenvironment and clinical outcomes across 13 cancer types.

The paper develops eTumorRisk, a network-based algorithm that predicts cancer risk using germline genome data, achieving 74.1-91.7% accuracy for six cancers with low false-negative rates.