Matthew A. Reyna

MOSAIC identifies driver mutations in cancer by accounting for patient subgroup heterogeneity in mutual exclusivity analysis, improving detection of subgroup-specific patterns.

NetMix improves subnetwork identification by reducing statistical bias in altered subnetwork estimation, outperforming existing methods on simulated and real data.

The paper identifies germline genetic features associated with immune infiltration in solid tumors using TCGA data and develops a framework for studying inherited factors influencing immune microenvironment.

The paper introduces the Cancer LncRNA Census (CLC), a curated resource of lncRNAs with causative roles in cancer, and shows their genomic and evolutionary features.

ActivePathways integrates multivariate omics data to identify enriched pathways, revealing cancer driver genes and prognostic pathways in breast cancer.

The paper analyzes non-coding mutations across 2,583 cancer genomes to identify genes and pathways affected, finding clusters of interacting proteins and insights into cancer mechanisms.