Machine learning approaches for analyzing Multiple Sclerosis transcriptomics data can be combined with universal persistent Brownian motion models to identify novel cellular force-generating mechanisms in diseased tissues.
Adversarial Debate Score
50% survival rate under critique
Expert panel critique
Independent views, each critiquing the hypothesis on its own — the score rewards genuine disagreement and discounts consensus.
Supporting Research Papers
- Machine Learning for analysis of Multiple Sclerosis cross-tissue bulk and single-cell transcriptomics data
Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system whose molecular mechanisms remain incompletely understood. In this study, we developed an end-to-end machine learn...
- Universal Persistent Brownian Motions in Confluent Tissues
Biological tissues are active materials whose non-equilibrium dynamics emerge from distinct cellular force-generating mechanisms. Using a two-dimensional active foam model, we compare the effects of t...
- Uncovering statistical structure in large-scale neural activity with Restricted Boltzmann Machines
Large-scale electrophysiological recordings now allow simultaneous monitoring of thousands of neurons across multiple brain regions, revealing structured variability in neural population activity. Und...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.