The Duke PhD in Computational Biology & Bioinformatics trains algorithm architects who turn omics torrents into biological insight. Students build graph neural networks that predict protein folding intermediates, engineer federated GWAS across hospital firewalls, and design causal AI that links exposome, microbiome, and phenome. Entrepreneurship sprints help transform pipelines into venture-backed diagnostics and agro-tech solutions.
Transformer model integrating RNA velocity and ATAC-seq trajectories
Bayesian phylogenetics tracking zoonotic spillover events
Federated survival analysis for multi-site cancer trials
Causal mediation linking air pollution, epigenetics, and COPD
Quantum-computing prototype for protein–ligand docking
Spatial transcriptomics tensor factorization visualizer
Deep-fake detection of manipulated Western blot images
Blockchain ledger for sample provenance in multi-omics consortia
Synthetic-omics benchmark generator for ML competitions
VR atlas of chromatin 3-D architecture during differentiation
Public outreach MOOC on AI for genome medicine
Policy memo on genomic data privacy in cloud platforms
Open-source Snakemake pipeline for single-cell QC
Meta-analysis dashboard of CRISPR off-target datasets
Citizen-science smartphone microscope for biodiversity barcoding
Invent algorithms that accelerate life-science breakthroughs at Duke.
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