Machine learning pipelines for analyzing Multiple Sclerosis transcriptomics can incorporate physically-informed subgraph isomorphism approaches from quantum molecular docking to identify novel disease biomarkers.
Adversarial Debate Score
38% survival rate under critique
Model Critiques
Supporting Research Papers
- A Physically-Informed Subgraph Isomorphism Approach to Molecular Docking Using Quantum Annealers
Molecular docking is a crucial step in the development of new drugs as it guides the positioning of a small molecule (ligand) within the pocket of a target protein. In the literature, a feasibility st...
- Resource-efficient Quantum Algorithms for Selected Hamiltonian Subspace Diagonalization
Quantum algorithms for selecting a subspace of Hamiltonians to diagonalize have emerged as a promising alternative to variational algorithms in the NISQ era. So far, such algorithms, which include the...
- Onset of Ergodicity Across Scales on a Digital Quantum Processor
Understanding how isolated quantum many-body systems thermalize remains a central question in modern physics. We study the onset of ergodicity in a two-dimensional disordered Heisenberg Floquet model ...
- 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.