Datasets
Healthy immune-cell data, cancer-infiltrating immune-cell data and non-immune-cell datasets.
Task
cell-type annotation
Cell-type annotation evaluates representations on datasets selected to be separate from pretraining data.
Sources
GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50Overview
Metrics
Table 1 reports precision, recall and macro F1 separately for human immune cells and held-out non-immune cells. The table describes its ± terms as confidence intervals across three random initializations of test splits; the caption does not name a confidence level.
Sources
GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Table 1 caption and column headingsAllowed inputs
Single-cell expression representations.
Sources
GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50Evaluation procedure diagram
How it worksComputational evaluation flow
Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol.
Sources
GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50; Table 1 caption and column headingslimited source coverage · Automated source review, 2026-09-16. All specifications and missing details
Results
Results are available, but no reviewed comparison panel is linked in this release.
All evaluations
1 evaluation · 1 result. Different protocols are not a single leaderboard.
Filter evaluations
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: GREmLN | Task: cell-type annotation Dataset: non-immune cells | 0.937 F1 fraction · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceZero-shot cell-type annotation using pre-trained cellular graph foundation model Aggregation: Not reported GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Table 2, Cell type annotation(zero-shot), Non-immune cells, F1 row, GREmLN column |
Source checking is not independent reproduction. Release 2026-09-29-06401fd5b220.
Methods and evaluation design
Procedure, tasks and evaluated configurations
How it works
Evaluation methodology
Healthy immune-cell data, cancer-infiltrating immune-cell data and non-immune-cell datasets. The source states that evaluation datasets do not overlap with pretraining datasets. Table 1 reports precision, recall and macro F1 separately for human immune cells and held-out non-immune cells. The table describes its ± terms as confidence intervals across three random initializations of test splits; the caption does not name a confidence level. The human immune-cell experiment compares GREmLN with scGPT, scFoundation and Geneformer. scGPT is excluded from the held-out non-immune-cell comparison because its CELLxGENE pretraining prevents that evaluation from being zero-shot. Test-cell regulatory graphs combine training-derived graphs using a training-fitted classifier rather than true test-cell labels.
Sources
GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50; Table 1 caption and column headings; Validation Experiments: Training cell type classifier; Table 1Recorded evaluations
Each evaluation records what was tested and under which conditions.
Run instructions
No runnable recipe has been reviewed for this task. Dataset access, model requirements, licences and compute requirements must be checked against its sources before execution.
A task describes a biological question. Choose a linked protocol to obtain concrete split and scoring instructions.
Strengths, limitations and unresolved questions
Strengths and limitations
Strengths and considerations
No source-reviewed explanatory claims are recorded here yet.
Limitations and conditions
- The immune-cell and held-out non-immune-cell settings have different comparator eligibility. scGPT’s absence from the latter is a pretraining-overlap control, not a failed prediction.
Sources
GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Table 1 caption and column headings; Validation Experiments: Training cell type classifier; Table 1
Profile review details
Targeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change.
Stable record: reported-task-6312c8a7ac045eSpecifications
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 |
|---|---|
| Datasets | Healthy immune-cell data, cancer-infiltrating immune-cell data and non-immune-cell datasets.SourcesGREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50 |
| Splits | The source states that evaluation datasets do not overlap with pretraining datasets.SourcesGREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50 |
| Metrics | Table 1 reports precision, recall and macro F1 separately for human immune cells and held-out non-immune cells. The table describes its ± terms as confidence intervals across three random initializations of test splits; the caption does not name a confidence level.SourcesGREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Table 1 caption and column headings |
| Baselines | The human immune-cell experiment compares GREmLN with scGPT, scFoundation and Geneformer. scGPT is excluded from the held-out non-immune-cell comparison because its CELLxGENE pretraining prevents that evaluation from being zero-shot.SourcesGREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Validation Experiments: Training cell type classifier; Table 1 |
| Leakage controls | Test-cell regulatory graphs combine training-derived graphs using a training-fitted classifier rather than true test-cell labels.SourcesGREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50 |
| Uncertainty | The cited text-accessible evaluation sections give no confidence-interval, resampling or repeat-run error-bar specification. Image-only tables and uninspected supplements are outside this absence claim. · Not reported in inspected sourcesSourcesGREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50 |
| Entity type | Paper-specific computational evaluation protocol.SourcesGREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50 |
| Organisms | Human immune cells and separately held-out non-immune cell collections.SourcesGREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50 |
| Assays | Single-cell expression with cell-type annotations.SourcesGREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50 |
| Allowed inputs | Single-cell expression representations.SourcesGREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50 |
| Adaptation | Zero-shot cell-type annotation is evaluated on held-out non-immune cells; fine-tuned perturbation-label prediction is a separate task.SourcesGREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50 |
Evidence
Source checking verifies the cited claim or transcription. It does not establish independent reproduction.
Papers and result coverage
Last literature check: 2026-09-17. Dated primary-source discovery and protocol/table screening. Source checking does not mean experimental reproduction. Only separately extracted and independently reviewed numeric batches are publishable.
| Paper or primary resource | Version | Reference |
|---|---|---|
| GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model | preprint version in PMC | Read source |
Historical gaps recorded on 2026-09-17
The catalogue now holds 1 result rows for this benchmark. A note below about pending extraction describes the state on 2026-09-17 and may since have been answered by a later batch. The result rows and their sources are the current record.
- complete numerical transcription and independent cell review: Full primary artifact and table inventory preserved; no new numeric row is published from this audit alone.
- exact checkpoint hashes and per-method scored denominators: Table labels alone do not establish these fields; do not infer checkpoint or scored count from model name or dataset size.
Search and extraction details
primary comparison tables located
Searches
- GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model 10.1101/2025.07.03.663009
Evidence locations
- Table 1:; XML table T1
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.
17 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Diagram caption Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol. Individual claims | GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50; Table 1 caption and column headings Version: preprint version in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Diagram steps
| GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50; Table 1 caption and column headings Version: preprint version in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Computational evaluation flow Individual claims | GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50; Table 1 caption and column headings Version: preprint version in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Datasets Healthy immune-cell data, cancer-infiltrating immune-cell data and non-immune-cell datasets. Individual claims | GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50 Version: preprint version in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Splits The source states that evaluation datasets do not overlap with pretraining datasets. Individual claims | GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50 Version: preprint version in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Adaptation Zero-shot cell-type annotation is evaluated on held-out non-immune cells; fine-tuned perturbation-label prediction is a separate task. Individual claims | GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50 Version: preprint version in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Metrics Table 1 reports precision, recall and macro F1 separately for human immune cells and held-out non-immune cells. The table describes its ± terms as confidence intervals across three random initializations of test splits; the caption does not name a confidence level. Individual claims | GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model Table 1 caption and column headings Version: preprint version in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Baselines The human immune-cell experiment compares GREmLN with scGPT, scFoundation and Geneformer. scGPT is excluded from the held-out non-immune-cell comparison because its CELLxGENE pretraining prevents that evaluation from being zero-shot. Individual claims | GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model Validation Experiments: Training cell type classifier; Table 1 Version: preprint version in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Leakage controls Test-cell regulatory graphs combine training-derived graphs using a training-fitted classifier rather than true test-cell labels. Individual claims | GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50 Version: preprint version in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Uncertainty The cited text-accessible evaluation sections give no confidence-interval, resampling or repeat-run error-bar specification. Image-only tables and uninspected supplements are outside this absence claim. Individual claims | GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50 Version: preprint version in PMC | unreported automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
View linked audit checks and correction history
Release 2026-09-29-06401fd5b220 · Record review: needs review
2 source records and release history
- GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Original source · preprint version in PMC
- GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model · Original source · preprint version in PMC
Technical metadata and extraction receipts
Stable ID: reported-task-6312c8a7ac045e
- areas
- cells-tissues
- tasks
- cell-type annotation
- entity level
- task
- version
- Not reported
- task
- cell-type annotation
- scope note
- Paper-specific evaluation task; protocol completeness requires further extraction.
- benchmark research
- review date: 2026-09-17; status: primary_comparison_tables_located; primary sources: evidence-expansion-gremln-2026-3a20c4ed; inspected locators: Table 1:; XML table T1; searched queries: GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model 10.1101/2025.07.03.663009; gaps: complete numerical transcription and independent cell review: Full primary artifact and table inventory preserved; no new numeric row is published from this audit alone.; exact checkpoint hashes and per-method scored denominators: Table labels alone do not establish these fields; do not infer checkpoint or scored count from model name or dataset size.; claim scope: Dated primary-source discovery and protocol/table screening. Source checking does not mean experimental reproduction. Only separately extracted and independently reviewed numeric batches are publishable.
- historical missing metadata
- protocol version: 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
- benchmark
- entity classification
- review date: 2026-09-17; rationale: This source-scoped record identifies the biological prediction task and holds its paper context. Preserve the existing task identity; exact split, model adaptation and scoring remain in linked evaluations or separate protocol records.; source ids: gremln-2026; source locator: Evaluation Datasets; Transcriptomic Landscape Learning & Cell Type Annotation; Bayesian Graph Integration; cached text lines 39–50; ambiguities: A paper- or suite-specific task may constrain some inputs or metrics; that alone does not make it interchangeable with a complete versioned protocol. No protocol equivalence is inferred.; Some legacy profile Entity type facts use the generic phrase computational evaluation protocol. That boilerplate is not sufficient to establish a single fixed protocol identity or to merge this task with another protocol record.
Related records
- dataset: non-immune cells
- benchmark: GREmLN: cell-type annotation