Machine learning models trained on transcriptomic data from Multiple Sclerosis patients will achieve higher predictive accuracy when incorporating features derived from persistent Brownian motion metrics observed in confluent tissue dynamics.
Adversarial Debate Score
20% survival rate under critique
Model Critiques
Supporting Research Papers
- Proton Quantum Effects in H₃S Electronic Structure: A Multicomponent DFT study via Nuclear-Electronic Orbital Method
We investigate the impact of the quantum effects of protons on the electronic structure of high-pressure H₃S, a benchmark hydrogen-rich superconductor with a critical temperature (T_c) exceeding 200 K...
- Integrated techno-enviroeconomic and life-cycle assessment of a solar-green hydrogen hybrid system with industrial wastewater reuse.
- MQTE: A Measurement-Based Quantum Algorithm for Robust Energy Spectrum Estimation in the NISQ Era
Extracting energy spectra from quantum Hamiltonians is a fundamental task for quantum simulation, yet remains challenging on noisy intermediate-scale quantum (NISQ) devices. We propose Measured Quantu...
- 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...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.