Strengths supported by sources
- The implementation explicitly supports single-gene and multi-gene perturbation workflows.snap-stanford/GEARS official source · README.md: introduction and Important notes
model · family
GEARS
GEARS predicts transcriptional responses to genetic perturbations using single-cell perturbation-screen data.
Shared profile: GEARS. This page retains the exact record and its evaluation context.
At a glance
Explanatory profile: source reviewed · Automated source review, 2026-09-16. This does not change the review status of its results.
| Property | Description and evidence |
|---|---|
| Required evidence | Perturbation identities and cells per conditionsnap-stanford/GEARS official source · README.md: introduction and Important notes |
| Configuration in this record | published implementationsnap-stanford/GEARS official source · README.md: introduction and Important notes |
| Model type | Not extracted or verified for this record. |
| Training data | Not extracted or verified for this record. |
| Context limits | Not extracted or verified for this record. |
| Access | Not extracted or verified for this record. |
| Code licence | Not extracted or verified for this record. |
| Weights licence | Not extracted or verified for this record. |
How it works
Schematic of the documented input, computation and output; not an executable configuration.
Read the diagram as text
- Perturbation-screen cells
- Training perturbations
- GEARS predictor
- Requested perturbation
- Expression response
A task-specific model is trained on measured perturbations, then predicts gene-expression responses for requested single or combined perturbations. Training composition determines what generalisation question is being tested.
snap-stanford/GEARS official source · README.md: introduction and Important notesBenchmarks 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
Limitations and conditions
- The maintainers state that cross-cell-type transfer is unsupported and that reliable combinatorial prediction needs some combinatorial training data.snap-stanford/GEARS official source · README.md: introduction and Important notes
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: catalog-model-gearsSources and history
Release 2026-09-16-d74d282221a9 · Record review: discovered
- GEARS official resource · Original source · Not reported
- snap-stanford/GEARS official source · Original source · f374e43e197b295016d80395d7a54ddb81cc6769
Technical metadata and extraction receipts
Stable ID: catalog-model-gears
- areas
- cells-tissues
- method types
- specialist
- entity level
- family
- version
- published implementation
- reported name
- GEARS
- access
- Public code; task-specific training data required.
- method type
- specialist
- missing metadata
- checkpoint revision: not_yet_extracted; training data: not_yet_extracted; licence: not_yet_extracted
Related records
- family: GEARS
- subject: GEARS: family GEARS