Laddaphone/accelerating-returns-panel-design

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

Contents (73 files)

Data

• data/parsed_networks.pkl — 285 parsed Boolean regulatory networks from 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

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