Upload data, results, and figures

README.md

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+---
+license: mit
+task_categories:
+  - tabular-classification
+tags:
+  - biology
+  - boolean-networks
+  - gene-regulation
+  - systems-biology
+  - experimental-design
+  - panel-design
+pretty_name: Accelerating Returns in Gene Panel Design
+size_categories:
+  - n<1K
+---
+
+# AcceleratingReturns: Data & Results
+
+Checkpoints, results, and figures for: **Accelerating Returns in Gene Panel Design: A Completion Theory for Regulatory Network Identification**  
+*BioSystems* (Elsevier), 2026.
+
+Code repository: [https://github.com/Laddaphone/AcceleratingReturns](https://github.com/Laddaphone/AcceleratingReturns)
+
+## Contents (73 files)
+
+### Data
+
+* `data/parsed_networks.pkl` — 285 parsed Boolean regulatory networks from [Biodivine Boolean Models](https://github.com/sybila/biodivine-boolean-models) (N=5–1076 genes). Each network is a dict with keys: `N`, `inputs`, `in_degrees`, `source_nodes`, `functions`, `nodes`, `filepath`.
+
+### Results — Original Submission
+
+* `results/original/` — 30 checkpoint files: theorem validation, strategy comparison, structural analysis, hitting sets, case study, ablation sweeps
+
+### Results — Revision Experiments
+
+* `results/revision/exp_protocol2.pkl` — Protocol 2 analysis (realistic observation budgets, n=171 networks)
+* `results/revision/exp_dropout.pkl` — Dropout robustness (p ∈ {0.05–0.50}, n=50 networks)
+* `results/revision/exp_scalability.pkl` — Computational scaling (N=50–2000, O(N³·¹⁵))
+* `results/revision/exp_full_comparison.pkl` — Full 285-network strategy comparison
+* `results/revision/exp_relaxed.pkl` — Error-tolerant (ε-relaxed) completion
+* `results/revision/exp_theorem_gap.pkl` — Theorem prediction vs algorithm performance
+
+### Figures
+
+* `figures/paper/` — 7 paper figures (PDF, 600 DPI)
+* `figures/revision/` — 14 revision figures (PDF + PNG)
+* `figures/diagnostics/` — 15 diagnostic/validation PNGs
+
+## Loading
+
+```python
+import pickle
+
+with open("data/parsed_networks.pkl", "rb") as f:
+    data = pickle.load(f)
+
+networks = data["parsed"]  # list of 285 dicts
+print(f"{len(networks)} networks, N={min(n['N'] for n in networks)}–{max(n['N'] for n in networks)}")
+
+# Load a revision experiment
+with open("results/revision/exp_dropout.pkl", "rb") as f:
+    dropout_results = pickle.load(f)
+```
+
+## Citation
+
+Douangnouanexay, L. (2026). Accelerating Returns in Gene Panel Design: A Completion Theory for Regulatory Network Identification. *BioSystems*.
+
+## License
+
+MIT

data/parsed_networks.pkl

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