Model type
Graph-based perturbation-response predictor
Configuration
GEARS
GEARS predicts transcriptional responses to single- and multi-gene perturbations from single-cell perturbation screens.
Sources (3)
snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_ModelHow it worksGEARS workflow
Conceptual summary of the documented data flow; optional inputs and configured downstream stages must be reported for a reproducible evaluation.
Sources (3)
snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_ModelOverview
Inputs
Single-cell expression data, perturbation labels and the graph resources used by the configured model.
Sources (3)
snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_ModelOutputs
Predicted gene-expression responses and genetic-interaction analyses.
Sources (3)
snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_ModelAccess
Official project documentation and implementation: https://github.com/snap-stanford/GEARS
Sources (3)
snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_Modellimited source coverage · Automated source review, 2026-09-16. All specifications and missing details
Evaluations and results
1 evaluation · 2 metric rows. Different protocols are not a single leaderboard.
Filter evaluations
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: GEARS | Protocol: GEARS Norman2019 CPA-control comparison MSE: Norman2019 perturbation-response MSE Dataset subset: Norman et al.2019 perturbation data used in GEARS Supplementary Table6 (GEARS Norman2019 CPA-control comparison split) | 0.216 ± 0.053 mse squared_expression_units_unreported · lower Uncertainty: type: unreported; reported spread: 0.053; note: Printed ± spread; SD/SE/CI type not established by inspected table caption. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · source checkedMethods, coverage and sourceGEARS on GEARS Norman2019 CPA-control comparison MSE: Norman2019 perturbation-response MSE Predict post-perturbation expression on Norman2019. MSE compares predicted and true expression; Pearson DE compares predicted versus true change over unperturbed controls (Supplementary Table1). Exact Table6 scoring gene subset and split manifest remain unextracted. Aggregation: Not reported GEARS primary supplement, Table6 · Supplementary Table6, printed p34 / PDF p35, row GEARS, column MSE |
| Configuration: GEARS | Protocol: GEARS Norman2019 CPA-control comparison Pearson DE: Norman2019 perturbation-response Pearson DE Dataset subset: Norman et al.2019 perturbation data used in GEARS Supplementary Table6 (GEARS Norman2019 CPA-control comparison split) | 0.556 ± 0.030 pearson_delta_expression correlation · higher Uncertainty: type: unreported; reported spread: 0.030; note: Printed ± spread; SD/SE/CI type not established by inspected table caption. Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · source checkedMethods, coverage and sourcePredict post-perturbation expression on Norman2019. MSE compares predicted and true expression; Pearson DE compares predicted versus true change over unperturbed controls (Supplementary Table1). Exact Table6 scoring gene subset and split manifest remain unextracted. Aggregation: Not reported GEARS primary supplement, Table6 · Supplementary Table6, printed p34 / PDF p35, row GEARS, column Pearson DE |
Source checking is not independent reproduction. Release 2026-09-23-2b89723c6dd9.
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How it works, versions and access
Related profile: GEARS. This page retains the exact record and its evaluation context.
This configuration
GEARS configuration as evaluated in GEARS Table6.
- record
- GEARS
- configuration
- Not reported
- entity type
- Configuration
How it works
How it works
GEARS predicts transcriptional responses to single- and multi-gene perturbations from single-cell perturbation screens. Two graph encoders represent gene coexpression and Gene Ontology perturbation similarity. Perturbation embeddings are composed with gene embeddings, then a cross-gene network and gene-specific decoders predict expression changes. The documented inputs are single-cell expression data, perturbation labels and the graph resources used by the configured model. The output consists of predicted gene-expression responses and genetic-interaction analyses.
Sources (3)
snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_ModelVersions and reproducibility
The README describes v0.1.1 updates; a specific trained checkpoint must be recorded separately. A gene-expression vector and perturbation set over the configured gene inventory; no fixed nucleotide or amino-acid token window.
Sources (3)
snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_ModelStrengths, limitations and unresolved questions
Strengths and limitations
Strengths and considerations
- Provides documented dataset/split handling and training interfaces for single and combinatorial perturbations.
Sources (3)
snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_Model
Limitations and conditions
- The authors explicitly warn against cross-cell-type transfer, bulk-RNA assumptions and predicting combinations after training only on single perturbations.
Sources (3)
snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_Model
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: catalog-model-gearsSpecifications
Inputs, training, access and other details
Explanatory profile: limited source coverage · 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 | Graph-based perturbation-response predictorSources (3)snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_Model |
| Architecture | Two graph encoders represent gene coexpression and Gene Ontology perturbation similarity. Perturbation embeddings are composed with gene embeddings, then a cross-gene network and gene-specific decoders predict expression changes.Sources (3)snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_Model |
| Inputs | Single-cell expression data, perturbation labels and the graph resources used by the configured model.Sources (3)snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_Model |
| Outputs | Predicted gene-expression responses and genetic-interaction analyses.Sources (3)snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_Model |
| Parameters | Configuration-dependent: gene and perturbation embedding tables grow with the selected gene/perturbation inventory, alongside graph and decoder parameters.Sources (3)snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_Model |
| Known versions | The README describes v0.1.1 updates; a specific trained checkpoint must be recorded separately.Sources (3)snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_Model |
| Training data | Fitted to the selected perturbation screen. Examples include Norman, Adamson and Dixit; the repository also lists Replogle RPE1/K562 loaders.Sources (3)snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_Model |
| Training cutoff | Inapplicable as a universal pretrained-model cutoff: GEARS fits the provided perturbation training set and builds its coexpression graph from that set. · Not applicableSources (3)snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_Model |
| Context limits | A gene-expression vector and perturbation set over the configured gene inventory; no fixed nucleotide or amino-acid token window.Sources (3)snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_Model |
| Weights licence | Separate checkpoint-distribution terms are not stated in the inspected release documentation and licence material. The source-code licence alone is not recorded as an explicit weight grant. · Not reported in inspected sourcesSources (4)snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML; snap-stanford/GEARS: LICENSE · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_Model; LICENSE: licence text |
| Access | Official project documentation and implementation: https://github.com/snap-stanford/GEARSSources (3)snap-stanford/GEARS: README.md; snap-stanford/GEARS: gears/model.py; gears: Journal full-text XML · GEARS paper Methods: Overview, Gene coexpression graph encoder, GO graph, Cross-gene effects and gene-specific decoder; gears/model.py: GEARS_Model |
| Code licence | MITSourcessnap-stanford/GEARS: LICENSE · LICENSE: licence text |
Evidence
Source checking verifies the cited claim or transcription. It does not establish independent reproduction.
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.
2 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Relationship: family catalog-model-gears Individual claims | GEARS primary supplement, Table6 Supplementary Table6, printed p34 / PDF p35, row GEARS; Supplementary Note5. Version: 10.1038/s41587-023-01905-6 publisher supplement | source checked automated source review · 2026-09-23 Audit detailsSource-backed evaluated identity only; no independent reproduction. Field: Claim: gears-2023-supp-table6-method-gears-catalog-model-gears-identity-claim Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Relationship: family discovery-model-gears Individual claims | GEARS primary supplement, Table6 Supplementary Table6, printed p34 / PDF p35, row GEARS; Supplementary Note5. Version: 10.1038/s41587-023-01905-6 publisher supplement | source checked automated source review · 2026-09-23 Audit detailsSource-backed evaluated identity only; no independent reproduction. Field: Claim: gears-2023-supp-table6-method-gears-discovery-model-gears-identity-claim Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
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Release 2026-09-23-2b89723c6dd9 · Record review: source checked
1 source records and release history
- GEARS primary supplement, Table6 · Original source · 10.1038/s41587-023-01905-6 publisher supplement
Technical metadata and extraction receipts
Stable ID: gears-2023-supp-table6-method-gears
- areas
- cells-spatial-multiomics
- source locator
- Supplementary Table6, printed p34 / PDF p35, row GEARS; Supplementary Note5.
- missing metadata
- checkpoint revision: unreported; parameters: unextracted
Related records
- family: GEARS
- family: GEARS
- model: GEARS on GEARS Norman2019 CPA-control comparison MSE: Norman2019 perturbation-response MSE
- model: GEARS on GEARS Norman2019 CPA-control comparison Pearson DE: Norman2019 perturbation-response Pearson DE
- subject: GEARS: family catalog-model-gears
- subject: GEARS: family discovery-model-gears