benchmark · suite

BEELINE

BEELINE evaluates gene regulatory network inference from single-cell expression data.

At a glance

Explanatory profile: source reviewed · Automated source review, 2026-09-16. This does not change the review status of its results.

Data, procedure and scoring
Property	Description and evidence
Record type	Benchmark framework and evaluatorMurali-group/Beeline official source · README: Usage / BLRunner.py, BLEvaluator.py, BLPlotter.py; Configuration
Inputs	Single-cell expression and a reference regulatory networkMurali-group/Beeline official source · README: Usage / BLRunner.py, BLEvaluator.py, BLPlotter.py; Configuration
Outputs and assessment	Ranked-edge AUPRC, AUROC and early precisionMurali-group/Beeline official source · README: Usage / BLRunner.py, BLEvaluator.py, BLPlotter.py; Configuration
Datasets	Not extracted or verified for this record.
Organisms	Not extracted or verified for this record.
Assays	Not extracted or verified for this record.
Splits	Not extracted or verified for this record.
Adaptation	Not extracted or verified for this record.
Baselines	Not extracted or verified for this record.

How it works

Procedure overview

Conceptual overview of the cited procedure; consult the pinned source for executable settings.

Read the diagram as text

Expression data
Infer ranked edges
Compare reference network
Report edge metrics

Murali-group/Beeline official source · README: Usage / BLRunner.py, BLEvaluator.py, BLPlotter.py; Configuration

Procedure

Run a selected inference algorithm, export its ranked regulatory edges and compare those edges with a supplied reference network. The framework separates execution, evaluation and plotting.

Murali-group/Beeline official source · README: Usage / BLRunner.py, BLEvaluator.py, BLPlotter.py; Configuration

Tested models and results

Release 2026-09-16-d74d282221a9 · 0 evaluations · 0 metric rows. Different protocols are not a single leaderboard.

Evaluated configurationMetricEvidence origin

No evaluations linked in this release.

Strengths and limitations

Strengths supported by sources

Containerized methods and a common evaluator make methodological comparisons inspectable.Murali-group/Beeline official source · README: Usage / BLRunner.py, BLEvaluator.py, BLPlotter.py; Configuration

Limitations and conditions

The chosen reference network defines what counts as a correct edge; this is not proof that every inferred interaction is causal.Murali-group/Beeline official source · README: Usage / BLRunner.py, BLEvaluator.py, BLPlotter.py; Configuration

Profile review details

Primary-source description checked by an automated research assistant. This is a profile review, not an independent execution or numerical reproduction.

Stable record: discovery-benchmark-beeline

Applicable tests and references

Applicability is distinct from a completed evaluation.

Random regulatory network · proposed
Tree-ensemble regulatory inference · proposed
GENIE3 · Proposed association
GRNBoost · Proposed association

Sources and history

Release 2026-09-16-d74d282221a9 · Record review: discovered

Murali-group/Beeline official source · Original source · 37464085eb8a95d6cc6a3d3a3c649d36db6052ed

Download this release

Technical metadata and extraction receipts

Stable ID: discovery-benchmark-beeline

areas: biological-networks
entity level: suite
missing metadata: dataset release: unextracted; metric implementation: unextracted; split manifest: unextracted; version: unextracted
scope note: Specialist molecular or omics evaluation; protocol details require review before numerical comparison.
task: Gene regulatory network inference
version: Not reported

Related records

applicable to: Random regulatory network
applicable to: Tree-ensemble regulatory inference
applicable to: GENIE3
applicable to: GRNBoost

Suggest a correction