ClarusC64/long-covid-recovery-interaction-geometry-v0.1
What this dataset does This dataset tests whether failed Long Covid recovery is better predicted by interaction geometry than by any single biological burden signal. The target is not disease diagnosis. The target is failed recovery. Core stability idea A patient may show high biological burden and still recover if repair capacity remains strong and omics layers stay coherent. A patient may show moderate burden and fail to recover if repair capacity weakens and… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/long-covid-recovery-interaction-geometry-v0.1.
What this dataset does
This dataset tests whether failed Long Covid recovery is better predicted by interaction geometry than by any single biological burden signal.
The target is not disease diagnosis.
The target is failed recovery.
Core stability idea
A patient may show high biological burden and still recover if repair capacity remains strong and omics layers stay coherent.
A patient may show moderate burden and fail to recover if repair capacity weakens and biological layers begin to diverge.
The central hypothesis is:
failed recovery = burden × disagreement × weak repair
Prediction target
label = 1
means failed or stuck recovery trajectory.
label = 0
means recovered or stable recovery trajectory.
Row structure
Each row represents a synthetic patient recovery state.
Columns:
scenario_id
ifn_signature
cytokine_activation
acute_phase_proteins
mitochondrial_stress
lipid_disruption
microbiome_instability
oxidative_stress
repair_response
symptom_score
label
Biological interpretation
The dataset is biology-shaped but synthetic.
It is designed to test whether models can detect interaction structure across immune, metabolic, microbiome, oxidative, and repair signals.
It does not claim patient-level validation.
Files
data/train.csv
data/test.csv
scorer.py
README.md
Evaluation
Submit predictions as:
scenario_id,prediction
LC101,0
LC102,0
LC103,1
Run:
python scorer.py predictions.csv data/test.csv
The scorer reports:
accuracy
precision
recall
f1
confusion matrix
Structural Note
This dataset is part of the Clarus / SIOS synthetic benchmark series.
It is designed to test whether models can identify latent stability geometry rather than rely on single-variable correlation.
The hidden structure is not intended to represent a clinical rule.
The purpose is to evaluate whether a model can reason across interacting biological signals.
Production Deployment
This dataset should not be used for clinical decision-making.
A production version would require validated patient-level Long Covid multi-omics data, longitudinal outcomes, and independent external testing.
Enterprise & Research Collaboration
This benchmark can be extended into real-data research using public or partner datasets containing transcriptomics, proteomics, metabolomics, immune profiling, symptom trajectories, and recovery outcomes.
License
MIT