William Kim

This paper introduces variable-sparsity matrix and tensor factorization algorithms for cancer genomics to improve biological interpretability and modeling of cancer subtypes and pathways.

The paper introduces the Multi-scale Copula Index (MCI), an information-theoretic measure of association, and demonstrates its utility in cancer genomics for capturing contextual associations in complex datasets.

The paper identifies six molecular subtypes of GIST using single nucleus RNA sequencing and correlates them with clinicopathologic features and clinical outcomes.

The paper detects H5N1 clade 2.3.4.4b influenza viruses in urban birds in New York City and highlights the urban animal-human interface as a potential zoonotic risk.

This paper introduces REVEALER 2.0, an improved algorithm for identifying genomic alterations associated with functional profiles, showing increased speed and resolution for analyzing cancer genomic data.