Translational Medical Science

Track urinalysis strips with your phone, build personal baselines, and learn change-point detection while testing early health signal patterns.

Build a risk model from step count and sleep regularity data, then test how well wearable signals predict new hypertension cases.

Train a cough classifier with MFCCs, spectrograms, and SHAP, then test how well phone audio can separate asthma, COPD, and post-viral cough.

Build a transparent risk stratification tool, then test how SHAP explanations affect usability, trust, and decision speed with real case vignettes.

Build a Markov model to compare wearable AFib screening and usual care, then test how costs, risk, and screening accuracy change outcomes.

Model amyloid-beta drug binding, rank repurposed candidates, and build a stronger research story with docking, rescoring, and literature checks.

Model how cholesterol changes LNP-mRNA membrane insertion and use free-energy outputs to rank formulations for brain-targeted delivery.

Build a matrix-factorization model to rank drug candidates, map off-target risk, and practice network analysis on diabetic nephropathy data.

Measure saliva pH and proxy signals with a smartphone to study how fasting and meal timing affect dental-caries risk in real people.

Test how music, breathing, and conditioning change pain tolerance and heart rate in a cold pressor task, then analyze the data like a real study.