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Gene Regulatory Network Inference from Expression and Perturbation Data

  • Task ID: biology.gene_network_timeseries
  • Domain: biology
  • Subdomain: systems_biology
  • Status: final
  • Benchmark set: seed42 (60 tasks)
  • Tags: gene_regulatory_network, network_inference, causal_inference, knockout, perturbation, systems_biology

Runtime and requirements

  • Estimated time: 30-60 minutes
  • Python: >=3.11
  • Packages: numpy>=2.0, scipy>=1.11, matplotlib>=3.7, scikit-learn>=1.3
  • GPU required: no
  • Network required: no

Public input and output contract

Inputs

  • system_info.json (data): Metadata: gene count, data types, sampling info
  • expression_timeseries.npy (data): Wild-type time-course expression data
  • perturbation_steady_states.npy (data): Steady-state profiles: row 0=wild-type, rows 1..M=multi-gene perturbation experiments

Outputs

  • analysis.py (code)
  • inferred_network.npy (data): Sparse inferred regulatory network (edge confidence matrix). Low-confidence edges should be exactly 0; off-diagonal nonzero density should be 5%-40%.
  • edge_sign_predictions.npy (data): Sign predictions: +1=activation, -1=inhibition, 0=no edge
  • hub_genes.json (data): Top hub regulator gene indices
  • method_comparison.json (data)
  • network_metrics.json (data)

Public repository files

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Provenance

This task is included in the current seed42 benchmark set at dataset revision f11a199f71fb4b854ac43a1bf548d5df519141a9.

This page is generated only from files tracked in the public ASI-Bench repository at commit f18382f03faf. Submission bundles, run logs, private scoring configuration, and private reference answers are not read by this page generator.