The Duke PhD in Biostatistics & Bioinformatics trains algorithm-builders who translate massive biomedical datasets into actionable insight. Candidates develop Bayesian deep-learning models for single-cell RNA-seq, federated survival analysis for multi-site cancer trials, and causal graphs linking exposome to chronic disease. Interdisciplinary clinics pair scholars with physicians to push methods directly into patient care.
Hierarchical model integrating eQTL and ATAC-seq for gene regulation inference
Differential-privacy survival analysis across hospital networks
Graph convolutional network predicting protein–drug interactions
Tensor factorization of spatial transcriptomics data
Causal mediation analysis of microbiome and obesity
Deep-learning harmonization of multi-vendor MRI scans
Synthetic-omics data generator for benchmark challenges
Interactive Shiny app visualizing variant pathogenicity
Policy brief on AI transparency in clinical decision support
Open-source Snakemake pipeline for single-cell QC and clustering
Bayesian nonparametric imputation of missing wearables data
Federated learning model predicting ICU sepsis onset
Blockchain ledger for reproducible bioinformatics workflows
VR data-storytelling exhibit of human cell atlas
Invent statistical tools that accelerate precision health at Duke.
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