Using machine learning pipelines for cross-tissue transcriptomic analysis to track the expression of genes involved in cellular force generation will predict the emergence of non-equilibrium dynamics observed in active biological tissues.
Adversarial Debate Score
57% 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
- 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...
- 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...
- Retrieval-Augmented Generation for Predicting Cellular Responses to Gene Perturbation
Predicting how cells respond to genetic perturbations is fundamental to understanding gene function, disease mechanisms, and therapeutic development. While recent deep learning approaches have shown p...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.