Model type
Gradient-boosted regulatory-network inference
Method
GRNBoost
GRNBoost infers gene-regulatory networks with distributed gradient-boosted regression.
Sources
aertslab/GRNBoost: README.md · README.md: Introduction and LicenseHow it worksGRNBoost workflow
Conceptual summary of the documented data flow; optional inputs and configured downstream stages must be reported for a reproducible evaluation.
Sources
aertslab/GRNBoost: README.md · README.md: Introduction and LicenseAt a glance
Inputs
Gene-expression data and candidate regulatory genes.
Sources
aertslab/GRNBoost: README.md · README.md: Introduction and LicenseOutputs
Predictive regulator-to-target links derived from the fitted regressions.
Sources
aertslab/GRNBoost: README.md · README.md: Introduction and LicenseSource reviewed · Automated source review, 2026-09-16. All specifications and missing details
Evaluations and results
Release 2026-09-17-d277315f7d76 · 0 evaluations · 0 metric rows. Different protocols are not a single leaderboard.
No evaluations linked in this release.
How it works
How it works
GRNBoost infers gene-regulatory networks with distributed gradient-boosted regression. Spark pipeline implementing target-wise regression with XGBoost, following the GENIE3 inference blueprint. The documented inputs are gene-expression data and candidate regulatory genes. The output consists of predictive regulator-to-target links derived from the fitted regressions.
Sources
aertslab/GRNBoost: README.md · README.md: Introduction and LicenseVersions and reproducibility
Spark/XGBoost GRNBoost implementation; do not substitute GRNBoost2 silently. Input gene matrix and distributed memory; no sequence token window.
Sources
aertslab/GRNBoost: README.md · README.md: Introduction and LicenseStrengths and limitations
Strengths supported by sources
- Distributes target-wise regression using Spark and replaces random forests with gradient boosting.
Sources
aertslab/GRNBoost: README.md · README.md: Introduction and License
Limitations and conditions
- This repository describes GRNBoost, not every later GRNBoost2 implementation. Predictive links remain hypotheses about regulation.
Sources
aertslab/GRNBoost: README.md · README.md: Introduction and License
Profile review details
Inspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied.
Stable record: discovery-model-grnboostSpecifications
Inputs, training, access and other details
Explanatory profile: source reviewed · Automated source review, 2026-09-16. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.
| Property | Description and evidence |
|---|---|
| Model type | Gradient-boosted regulatory-network inferenceSourcesaertslab/GRNBoost: README.md · README.md: Introduction and License |
| Architecture | Spark pipeline implementing target-wise regression with XGBoost, following the GENIE3 inference blueprint.Sourcesaertslab/GRNBoost: README.md · README.md: Introduction and License |
| Inputs | Gene-expression data and candidate regulatory genes.Sourcesaertslab/GRNBoost: README.md · README.md: Introduction and License |
| Outputs | Predictive regulator-to-target links derived from the fitted regressions.Sourcesaertslab/GRNBoost: README.md · README.md: Introduction and License |
| Parameters | Dataset- and boosting-configuration-dependent.Sourcesaertslab/GRNBoost: README.md · README.md: Introduction and License |
| Known versions | Spark/XGBoost GRNBoost implementation; do not substitute GRNBoost2 silently.Sourcesaertslab/GRNBoost: README.md · README.md: Introduction and License |
| Training data | Fitted to the supplied expression data.Sourcesaertslab/GRNBoost: README.md · README.md: Introduction and License |
| Training cutoff | Inapplicable to neural pretraining; reference-database and input-data dates must be recorded for each run. · Not applicableSourcesaertslab/GRNBoost: README.md · README.md: Introduction and License |
| Context limits | Input gene matrix and distributed memory; no sequence token window.Sourcesaertslab/GRNBoost: README.md · README.md: Introduction and License |
| Weights licence | No universal neural checkpoint; fitted regressors depend on the input dataset.Sourcesaertslab/GRNBoost: README.md · README.md: Introduction and License |
| Access | Official project documentation and implementation: https://github.com/aertslab/GRNBoostSourcesaertslab/GRNBoost: README.md · README.md: Introduction and License |
| Code licence | BSD-3-ClauseSourcesaertslab/GRNBoost: LICENSE.txt · LICENSE.txt: licence text |
| Assumptions | Not extracted or verified for this record. |
Applicable tests and references
Applicability is distinct from a completed evaluation.
- BEELINE · Proposed association
Evidence table
Inspect claims, sources and review details
Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
20 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Diagram caption Conceptual summary of the documented data flow; optional inputs and configured downstream stages must be reported for a reproducible evaluation. Individual claims | aertslab/GRNBoost: README.md README.md: Introduction and License Version: 26c836b3dcbb85852d3c6f4b8340e8655434da02 | source checked automated source review · 2026-09-16 Audit detailsInspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: Source artifact SHA-256: Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact |
| Diagram steps ["Expression and regulator set","Distributed target-wise boosting","Feature importance","Candidate regulatory network"] Individual claims | aertslab/GRNBoost: README.md README.md: Introduction and License Version: 26c836b3dcbb85852d3c6f4b8340e8655434da02 | source checked automated source review · 2026-09-16 Audit detailsInspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: Source artifact SHA-256: Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact |
| Diagram title GRNBoost workflow Individual claims | aertslab/GRNBoost: README.md README.md: Introduction and License Version: 26c836b3dcbb85852d3c6f4b8340e8655434da02 | source checked automated source review · 2026-09-16 Audit detailsInspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: Source artifact SHA-256: Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact |
| Model type Gradient-boosted regulatory-network inference Individual claims | aertslab/GRNBoost: README.md README.md: Introduction and License Version: 26c836b3dcbb85852d3c6f4b8340e8655434da02 | source checked automated source review · 2026-09-16 Audit detailsInspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: Source artifact SHA-256: Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact |
| Architecture Spark pipeline implementing target-wise regression with XGBoost, following the GENIE3 inference blueprint. Individual claims | aertslab/GRNBoost: README.md README.md: Introduction and License Version: 26c836b3dcbb85852d3c6f4b8340e8655434da02 | source checked automated source review · 2026-09-16 Audit detailsInspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: Source artifact SHA-256: Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact |
| Access Official project documentation and implementation: https://github.com/aertslab/GRNBoost Individual claims | aertslab/GRNBoost: README.md README.md: Introduction and License Version: 26c836b3dcbb85852d3c6f4b8340e8655434da02 | source checked automated source review · 2026-09-16 Audit detailsInspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: Source artifact SHA-256: Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact |
| Code licence BSD-3-Clause Individual claims | aertslab/GRNBoost: LICENSE.txt LICENSE.txt: licence text Version: 26c836b3dcbb85852d3c6f4b8340e8655434da02 | source checked automated source review · 2026-09-16 Audit detailsInspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: Source artifact SHA-256: Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact |
| Inputs Gene-expression data and candidate regulatory genes. Individual claims | aertslab/GRNBoost: README.md README.md: Introduction and License Version: 26c836b3dcbb85852d3c6f4b8340e8655434da02 | source checked automated source review · 2026-09-16 Audit detailsInspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: Source artifact SHA-256: Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact |
| Outputs Predictive regulator-to-target links derived from the fitted regressions. Individual claims | aertslab/GRNBoost: README.md README.md: Introduction and License Version: 26c836b3dcbb85852d3c6f4b8340e8655434da02 | source checked automated source review · 2026-09-16 Audit detailsInspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: Source artifact SHA-256: Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact |
| Parameters Dataset- and boosting-configuration-dependent. Individual claims | aertslab/GRNBoost: README.md README.md: Introduction and License Version: 26c836b3dcbb85852d3c6f4b8340e8655434da02 | source checked automated source review · 2026-09-16 Audit detailsInspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: Source artifact SHA-256: Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact |
Sources and history
Release 2026-09-17-d277315f7d76 · Record review: discovered
3 source records and release history
- aertslab/GRNBoost official source · Original source · 26c836b3dcbb85852d3c6f4b8340e8655434da02
- aertslab/GRNBoost: README.md · Original source · 26c836b3dcbb85852d3c6f4b8340e8655434da02
- aertslab/GRNBoost: LICENSE.txt · Original source · 26c836b3dcbb85852d3c6f4b8340e8655434da02
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
- reported name
- GRNBoost
- version
- Not reported
- historical missing metadata
- checkpoint: unextracted; code licence: unextracted; parameters: unextracted; training cutoff: unextracted; training data: unextracted; version: unextracted; weights licence: unextracted
- metadata review scope
- historical_missing_metadata preserves the original discovery state. Current descriptive evidence and missingness are recorded in profile.facts; numerical-result review is separate.
- legacy kinds
- model
- entity classification
- review date: 2026-09-17; rationale: The cited architecture/procedure describes an algorithm, representation recipe or software toolkit, rather than a single released learned biological model. Fitted models, selected reference databases and concrete runs remain separate configurations.; source ids: evidence-official-099d109417c7fae96e96; source locator: README.md: Introduction and License; ambiguities: This procedure fits statistical learners for a supplied expression matrix; that does not make the general inference algorithm a single pretrained model checkpoint.
Related records
- applicable to: BEELINE