Scalable Hierarchical Bayesian Models for Pharmaceutical-Scale Genomic Data
Roche
The Challenge
Roche needed to apply Bayesian statistical models to large-scale pharmaceutical data — the kind of datasets that are routine in genomics and clinical development but that break traditional Bayesian computation. With tens of thousands of parameters and hundreds of thousands of observations, standard approaches would take days to produce results. For Bayesian methods to be practical in pharmaceutical R&D workflows, they needed to run in hours.
Our Approach
We designed a hierarchical model from the ground up for computational efficiency at this scale. The model's structure reflects the natural groupings in pharmaceutical data while using techniques that help the sampling algorithm navigate a 34,000-dimensional space without getting stuck.
The key was pairing careful statistical modeling with modern computational infrastructure. By running the inference on GPUs and using a backend optimized for parallel computation, we brought what would have been a multi-day computation down to something that fits in a lunch break.
Results
The model fit its 34,000 parameters across 250,000 observations in just over an hour. It demonstrated something important for the broader pharmaceutical industry: Bayesian methods aren't limited to small, boutique analyses. With the right implementation, they scale to the same datasets where frequentist methods have traditionally dominated by default.
For Roche, it opened the door to applying principled uncertainty quantification across their large-scale data assets.
PyMC Labs Team
- Thomas Wiecki
- Maxim
- Adrian
- Niall
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