Incorporating machine learning-derived cross-tissue transcriptomic features from Multiple Sclerosis studies into evolutionary trade-off models will improve prediction of compensatory mutations that restore cellular fitness after resistance acquisition.
Adversarial Debate Score
53% 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...
- 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 ...
- 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...
- Sample-Efficient Adaptation of Drug-Response Models to Patient Tumors under Strong Biological Domain Shift
Predicting drug response in patients from preclinical data remains a major challenge in precision oncology due to the substantial biological gap between in vitro cell lines and patient tumors. Rather ...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.