Configuration

scGPT

This single-cell foundation-model configuration is a comparator in the scELMo study.

Sources

scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Results/scELMo for clustering and batch effect correction. (paragraph 1); Methods/Data pre-processing. (paragraph 1)

1 evaluation · 1 metric row

How it worksEvaluated procedure (conceptual)

Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings.

Sources

scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods/scELMo under the fine-tuning framework. (paragraph 1); Methods/Data pre-processing./Metrics. (paragraph 12)

At a glance

Model type

Single-cell foundation model; this record is the paper-specific evaluated configuration.

Sources

bowang-lab/scGPT README.md · README.md model description

Biological inputs

Single-cell gene-expression measurements

Sources

scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods/Data pre-processing./Metrics. (paragraph 15); Methods/Data pre-processing./Metrics. (paragraph 12)

Outputs

Cell embeddings or adapted task predictions

Sources

scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods/Problem definition. (paragraph 4); Results/scELMo for clustering and batch effect correction. (paragraph 1)

limited source coverage · Automated source review, 2026-09-16. All specifications and missing details

Evaluations and results

Release 2026-09-17-d277315f7d76 · 1 evaluation · 1 metric row. Different protocols are not a single leaderboard.

Evaluation setupMetricEvidence origin

Results grouped by the exact reported evaluation
Metric and finding	Coverage and uncertainty	Evidence
scGPT: Cell-type annotation Configuration: scGPTTask: Cell-type annotationDataset: hPancreas Zero-shot setting; source caption says some comparator rows come from GenePT. Result quoted from another source · Evaluation metadata: needs review
0.550 F1 Unit: unitless · Direction: unknown	Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported	source checkedscELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Table 1, hPancreas zero-shot / scGPT (z) row, F1 column Source checking is not independent reproduction.

How it works

How the evaluated method works

Pretrained cell/gene representations are evaluated on the paper’s clustering, integration, annotation or perturbation tasks, with the associated evaluation retaining the task-specific procedure.

Sources

Underlying method and version boundaries

The official scGPT implementation supplies pretrained checkpoints and separate workflows for embedding extraction, cell annotation, integration and perturbation modelling. The checkpoint and adaptation procedure must be identified separately for each result.

Sources

bowang-lab/scGPT README.md · README.md; introduction, model description, pretrained-model and usage sections at pinned revision

What was evaluated

The linked evaluation record identifies scGPT: Cell-type annotation. Its dataset, split, adaptation and evidence origin remain attached to the reported results.

Sources

scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-029

Strengths and limitations

Strengths and considerations

Provides a learned-expression reference for comparison with text-derived biological embeddings.
Sources
scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods/scELMo under the fine-tuning framework. (paragraph 4); Discussion (paragraph 2)

Limitations and conditions

Different downstream tasks and adaptation settings cannot be combined into a single model-quality claim.
Sources
scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Introduction (paragraph 3); Results/Overview of scELMo. (paragraph 2)

Profile review details

Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction.

Stable record: reported-model-77ad27d4098177

Specifications

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.

Inputs, outputs and configuration
Property	Description and evidence
Model type	Single-cell foundation model; this record is the paper-specific evaluated configuration. Sources bowang-lab/scGPT README.md · README.md model description
Architecture / procedure	Pretrained cell/gene representations are evaluated on the paper’s clustering, integration, annotation or perturbation tasks, with the associated evaluation retaining the task-specific procedure. Sources scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods/scELMo under the fine-tuning framework. (paragraph 1); Methods/Data pre-processing./Metrics. (paragraph 12)
Biological inputs	Single-cell gene-expression measurements Sources scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods/Data pre-processing./Metrics. (paragraph 15); Methods/Data pre-processing./Metrics. (paragraph 12)
Outputs	Cell embeddings or adapted task predictions Sources scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods/Problem definition. (paragraph 4); Results/scELMo for clustering and batch effect correction. (paragraph 1)
Parameters	An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sources Sources (2) scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis; bowang-lab/scGPT README.md · Methods/Problem definition.; Methods/Method explanation.; Methods/scELMo under the zero-shot learning framework.; Methods/scELMo under the fine-tuning framework.; Methods/Data pre-processing.; Methods/Data pre-processing./Metrics.; Methods/Explanations of baseline models.; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision
Known versions / configuration	scGPT is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sources Sources scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label.
Training data / fitting	The comparison includes frozen and fine-tuned embedding settings followed by k-nearest-neighbour classification. Table 1 notes that some results were taken from GenePT; the exact origin and adaptation must therefore remain attached to each row. Sources scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods / Explanations of baseline models; Table 1 caption
Context limits	A maximum input/context length for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sources Sources (2) scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis; bowang-lab/scGPT README.md · Methods/Problem definition.; Methods/Method explanation.; Methods/scELMo under the zero-shot learning framework.; Methods/scELMo under the fine-tuning framework.; Methods/Data pre-processing.; Methods/Data pre-processing./Metrics.; Methods/Explanations of baseline models.; inspected for explicit maximum input length (dataset lengths and family-wide limits are not substituted); README.md at pinned repository revision
Access	Official upstream implementation and usage documentation: https://github.com/bowang-lab/scGPT/blob/cebd6fae655b9c585a4807daa3ac31bb764f06b4/README.md. This pinned documentation revision is not automatically the evaluated weight revision. Sources bowang-lab/scGPT README.md · README.md; installation, model download and usage instructions
Code licence	MIT (upstream repository code at the cited revision; this does not establish every dependency or historical checkpoint licence). Sources bowang-lab/scGPT LICENSE · LICENSE; complete licence text
Weights licence	The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. · Not reported in inspected sources Sources bowang-lab/scGPT README.md · README.md; checkpoint/access documentation and licence scope

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.

22 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-09-17-d277315f7d76
Property and statement	Original source and location	Review and provenance
Diagram caption Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings. Individual claims	scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis Original source ↗ Methods/scELMo under the fine-tuning framework. (paragraph 1); Methods/Data pre-processing./Metrics. (paragraph 12) Version: preprint archived 2025-08-23 Retrieved: 2026-09-16T10:41:16.537541+00:00	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.diagram.caption` Source artifact SHA-256: `ef75f0d63a567f5e9d7132fd847f44838a82a9741ae55323437e1d1812d86316` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram steps ["Single-cell gene-expression measurements","scGPT","Cell embeddings or adapted task predictions"] Individual claims	scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis Original source ↗ Methods/scELMo under the fine-tuning framework. (paragraph 1); Methods/Data pre-processing./Metrics. (paragraph 12) Version: preprint archived 2025-08-23 Retrieved: 2026-09-16T10:41:16.537541+00:00	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.diagram.steps` Source artifact SHA-256: `ef75f0d63a567f5e9d7132fd847f44838a82a9741ae55323437e1d1812d86316` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram title Evaluated procedure (conceptual) Individual claims	scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis Original source ↗ Methods/scELMo under the fine-tuning framework. (paragraph 1); Methods/Data pre-processing./Metrics. (paragraph 12) Version: preprint archived 2025-08-23 Retrieved: 2026-09-16T10:41:16.537541+00:00	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.diagram.title` Source artifact SHA-256: `ef75f0d63a567f5e9d7132fd847f44838a82a9741ae55323437e1d1812d86316` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Model type Single-cell foundation model; this record is the paper-specific evaluated configuration. Individual claims	bowang-lab/scGPT README.md Original source ↗ README.md model description Version: cebd6fae655b9c585a4807daa3ac31bb764f06b4 Retrieved: 2026-09-16T20:00:00.816587+00:00	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.0.value` Source artifact SHA-256: `b0503e8ca789f19f1fc2350c5aaf57b1b323bbae43b354655231b5f4a1586c83` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Architecture / procedure Pretrained cell/gene representations are evaluated on the paper’s clustering, integration, annotation or perturbation tasks, with the associated evaluation retaining the task-specific procedure. Individual claims	scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis Original source ↗ Methods/scELMo under the fine-tuning framework. (paragraph 1); Methods/Data pre-processing./Metrics. (paragraph 12) Version: preprint archived 2025-08-23 Retrieved: 2026-09-16T10:41:16.537541+00:00	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.1.value` Source artifact SHA-256: `ef75f0d63a567f5e9d7132fd847f44838a82a9741ae55323437e1d1812d86316` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Weights licence The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. Individual claims	bowang-lab/scGPT README.md Original source ↗ README.md; checkpoint/access documentation and licence scope Version: cebd6fae655b9c585a4807daa3ac31bb764f06b4 Retrieved: 2026-09-16T20:00:00.816587+00:00	unreported automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.10.value` Source artifact SHA-256: `b0503e8ca789f19f1fc2350c5aaf57b1b323bbae43b354655231b5f4a1586c83` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Biological inputs Single-cell gene-expression measurements Individual claims	scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis Original source ↗ Methods/Data pre-processing./Metrics. (paragraph 15); Methods/Data pre-processing./Metrics. (paragraph 12) Version: preprint archived 2025-08-23 Retrieved: 2026-09-16T10:41:16.537541+00:00	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.2.value` Source artifact SHA-256: `ef75f0d63a567f5e9d7132fd847f44838a82a9741ae55323437e1d1812d86316` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Outputs Cell embeddings or adapted task predictions Individual claims	scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis Original source ↗ Methods/Problem definition. (paragraph 4); Results/scELMo for clustering and batch effect correction. (paragraph 1) Version: preprint archived 2025-08-23 Retrieved: 2026-09-16T10:41:16.537541+00:00	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.3.value` Source artifact SHA-256: `ef75f0d63a567f5e9d7132fd847f44838a82a9741ae55323437e1d1812d86316` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims	bowang-lab/scGPT README.md Original source ↗ Methods/Problem definition.; Methods/Method explanation.; Methods/scELMo under the zero-shot learning framework.; Methods/scELMo under the fine-tuning framework.; Methods/Data pre-processing.; Methods/Data pre-processing./Metrics.; Methods/Explanations of baseline models.; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: cebd6fae655b9c585a4807daa3ac31bb764f06b4 Retrieved: 2026-09-16T20:00:00.816587+00:00	unreported automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.4.value` Source artifact SHA-256: `b0503e8ca789f19f1fc2350c5aaf57b1b323bbae43b354655231b5f4a1586c83` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims	scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis Original source ↗ Methods/Problem definition.; Methods/Method explanation.; Methods/scELMo under the zero-shot learning framework.; Methods/scELMo under the fine-tuning framework.; Methods/Data pre-processing.; Methods/Data pre-processing./Metrics.; Methods/Explanations of baseline models.; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: preprint archived 2025-08-23 Retrieved: 2026-09-16T10:41:16.537541+00:00	unreported automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.4.value` Source artifact SHA-256: `ef75f0d63a567f5e9d7132fd847f44838a82a9741ae55323437e1d1812d86316` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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Sources and history

Release 2026-09-17-d277315f7d76 · Record review: needs review

3 source records and release history

scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Original source · preprint archived 2025-08-23
bowang-lab/scGPT README.md · Original source · cebd6fae655b9c585a4807daa3ac31bb764f06b4
bowang-lab/scGPT LICENSE · Original source · cebd6fae655b9c585a4807daa3ac31bb764f06b4

Download this release

Technical metadata and extraction receipts

Stable ID: reported-model-77ad27d4098177

areas: cells-tissues
entity level: method
version: Not reported
reported name: scGPT
historical missing metadata: version: not_reported_in_legacy_extract; checkpoint revision: not_reported_in_legacy_extract; training data: not_reported_in_legacy_extract; licence: not_reported_in_legacy_extract
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: This source-scoped entry preserves the method/configuration actually named in an evaluation. It is neither a global family identity nor proof of an immutable checkpoint; the linked evaluation retains adaptation, fitting and scoring details.; source ids: scelmo-2025; evidence-reported-base-scgpt-readme-md; source locator: Methods/scELMo under the fine-tuning framework. (paragraph 1); Methods/Data pre-processing./Metrics. (paragraph 12) | README.md model description | Results/scELMo for clustering and batch effect correction. (paragraph 1); Methods/Data pre-processing. (paragraph 1); ambiguities: Configuration means the source-labelled evaluated identity. It does not establish missing checkpoint hashes, default settings or equivalence to same-named records in other papers.

Related records

model: scGPT: Cell-type annotation

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