Statistical methods and machine learning approaches for detection of tumor-derived DNA based on genome-wide cell-free DNA fragmentation patterns. Applications include the early detection of cancer and monitoring disease in late stage cancer patients.
Methods and software for modeling technical and biological variation within and between large-scale studies with high-throughput data. Applications include Bayesian hierarchical models for differential gene expression analysis in multiple studies and Bayesian hierarchical models for copy number estimation.
Modeling the contribution of molecular characteristics to cancer risk using models that incorporate measurement error of genomic variants.
Bayesian approaches to model uncertainty in evolutionary models of cancer progression.
Statistical models for co-occurrence of somatic sequence variants, copy number variants, and rearrangements in large-scale databases.