Zihang Wen

CNVeil infers haplotype-specific copy number and subclonal architecture from single-cell cancer genomes, outperforming existing methods in accuracy and resolution.

This paper maps gene drive dynamics onto Mendelian models to enable classical population genetics tools for predicting gene-drive outcomes.

This paper uses reinforcement learning to design adaptive antibiotic treatment strategies that overcome phenotypic heterogeneity in bacterial populations.

CNVeil improves tumor subclone identification and copy number estimation in single-cell DNA sequencing by addressing noise and bias through novel computational methods.