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model · method

GRNBoost

GRNBoost infers candidate regulatory networks by predicting gene expression with boosted trees.

0 evaluations · 0 metric rows

At a glance

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

Inputs, outputs and configuration
PropertyDescription and evidence
Implementation distinctionThis source describes GRNBoost; GRNBoost2 is not silently substitutedaertslab/GRNBoost official source · README.md: What is GRNBoost? and algorithm description
Model typeNot extracted or verified for this record.
Known versionsNot extracted or verified for this record.
Training dataNot extracted or verified for this record.
Context limitsNot extracted or verified for this record.
AccessNot extracted or verified for this record.
Code licenceNot extracted or verified for this record.
Weights licenceNot extracted or verified for this record.

How it works

Conceptual procedure

Schematic of the documented input, computation and output; not an executable configuration.

Conceptual procedureExpression matrix. Then: Candidate regulators. Then: Per-gene boosted regressions. Then: Feature importance. Then: Candidate regulatory networkExpression matrixCandidate regulatorsPer-gene boosted regressionsFeature importanceCandidate regulatory network
Read the diagram as text
  1. Expression matrix
  2. Candidate regulators
  3. Per-gene boosted regressions
  4. Feature importance
  5. Candidate regulatory network
aertslab/GRNBoost official source · README.md: What is GRNBoost? and algorithm description

For each target gene, expression from candidate regulators is used in a regression. Feature importance becomes evidence for candidate regulatory edges; the implementation distributes these regressions with Spark.

aertslab/GRNBoost official source · README.md: What is GRNBoost? and algorithm description

Benchmarks and results

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

No evaluations linked in this release.

Strengths and limitations

Strengths supported by sources

Limitations and conditions

  • Predictive importance in observational expression data is not proof of a direct causal regulatory edge.aertslab/GRNBoost official source · README.md: What is GRNBoost? and algorithm description
Profile review details

Primary project documentation or paper inspected for the explanatory claims and cited locations. Reviewed coverage concerns this narrative, not complete metadata, independent reproduction or a performance ranking.

Stable record: discovery-model-grnboost

Applicable tests and references

Applicability is distinct from a completed evaluation.

Sources and history

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

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Technical metadata and extraction receipts

Stable ID: discovery-model-grnboost

areas
biological-networks
access
official_source_linked
benchmark applicability
candidate; not evidence of a reported evaluation
candidate benchmark ids
discovery-benchmark-beeline
entity level
method
missing metadata
checkpoint: unextracted; code licence: unextracted; parameters: unextracted; training cutoff: unextracted; training data: unextracted; version: unextracted; weights licence: unextracted
reported name
GRNBoost
version
Not reported
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