| --- |
| 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 |
|
|
