Machine learning pipelines trained on bulk and single-cell transcriptomics data from Multiple Sclerosis can be enhanced by including features derived from universal persistent Brownian motion parameters, improving disease subtype classification.
Adversarial Debate Score
40% survival rate under critique
Model Critiques
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...
- Cross-Species Transfer Learning for Electrophysiology-to-Transcriptomics Mapping in Cortical GABAergic Interneurons
Single-cell electrophysiological recordings provide a powerful window into neuronal functional diversity and offer an interpretable route for linking intrinsic physiology to transcriptomic identity. H...
- HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts
Single-cell perturbation studies face dual heterogeneity bottlenecks: (i) semantic heterogeneity--identical biological concepts encoded under incompatible metadata schemas across datasets; and (ii) st...
- Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability
Immune checkpoint inhibitors (ICIs) have transformed cancer therapy; yet substantial proportion of patients exhibit intrinsic or acquired resistance, making accurate pre-treatment response prediction ...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.