Integrating machine learning-derived biomarkers from Multiple Sclerosis transcriptomic data into evolutionary trade-off models will identify gene expression patterns that predict the emergence of drug-resistant immune cell phenotypes.
Adversarial Debate Score
53% 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...
- 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 ...
- Towards an Interpretable Machine Learning Model for Predicting Antimicrobial Resistance.
This paper explores the main stages of developing an interpretable machine learning (ML) model for predicting antimicrobial resistance (AMR), highlighting the importance of model interpretability in e...
- Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis
Mycobacterium tuberculosis (MTB) is a human-specific pathogen that primarily infects humans, causing tuberculosis (TB). Antimicrobial resistance (AMR) in MTB presents a formidable challenge to global ...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.