An integral part of protein structural modeling with Rosetta is evaluating the quality of predictions against experimental observations. The community effort to develop Scientific Benchmarks has been instrumental in tracking and measuring modeling progress. However, due to the complexity and uncertainty in both the model predictions and the biochemical data, it can be challenging for researchers from both biochemical and computational researchers without a strong statistical background to rigorously test prediction accuracy for their specific modeling tasks. To improve the scientific quality for Rosetta-based modeling we would like to hold a hands-on 2-day workshop aimed at teaching researchers in the Rosetta community practical best-practices for how to analyze their data using rigorous Bayesian statistical modeling.
The first day we will teach the basics of “tidy data” analysis in R and the BayesPharma analysis workflow built on Stan and brms. On the second day we will guide participants to work together to apply the statistical modeling methods to their own data analysis problems.
Day 1 Morning: tidy data analysis in R
Day 1 Afternoon: Bayesian model workflow
Day 2: Bring Your Own Data for hands-on analysis