William C. H. Cross

The paper develops a computational cell-cycle model to quantify SV generation and unify genomic patterns from chromosomal instability.

DeepCNA identifies cancer-specific copy number aberrations across 13 cancer types using explainable deep learning for diagnosis and biomarker discovery.

The paper presents a genetic model of central chondrosarcoma using genomic and epigenomic data to identify mutations and methylation patterns associated with patient outcomes.

This paper introduces CNAqc, a toolset for validating clonal and subclonal copy number alterations in bulk tumor sequencing by addressing tumor purity, ploidy, and heterogeneity using simulations and single-cell data.

This paper introduces a model-based subclonal reconstruction method that integrates evolutionary theory with machine learning to improve accuracy in tumor subclonal analysis.

The paper models tumor-immune dynamics in colorectal cancer progression, showing immune evasion enables transition from immune-hot adenomas to immune-cold carcinomas.