ML Research Assistant

Applying Sequential Monte Carlo and MCMC to model SCA7 disease progression from sparse clinical biomarker data.

What I'm Doing

I am applying Sequential Monte Carlo and MCMC sampling to model SCA7 disease progression. The work involves fitting patient-level biomarker trajectories from sparse longitudinal clinical data to quantify stage-level progression rates.

Impact (Expected)

Quantifying stage-level progression rates for SCA7 from clinical data could improve understanding of disease trajectory and inform clinical trial design.

What I'm Learning

I am gaining deep experience with Sequential Monte Carlo methods and MCMC sampling for Bayesian inference on sparse longitudinal data. The work requires handling irregular observation schedules and missing data in clinical biomarker records.

Key Highlights

• Applied Sequential Monte Carlo and MCMC sampling to model SCA7 disease progression, fitting patient-level biomarker trajectories from sparse longitudinal clinical data to quantify stage-level progression rates.