Datasets
M.S., Zheng68k, NSCLC and COVID-19 single-cell datasets.
Task
Cell-type identification
Cell-type identification compares parameter-efficient adaptation with conventional fine-tuning on external annotation datasets.
Sources
Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41Overview
Metrics
Accuracy, precision, recall and weighted F1; silhouette is an additional embedding diagnostic.
Allowed inputs
Single-cell gene-expression representations.
Sources
Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41Evaluation 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
Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41limited 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
2 evaluations · 2 results. Different protocols are not a single leaderboard.
Filter evaluations
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: scGPT | Task: Cell-type identification Dataset: M.S. single-cell dataset | 0.734 F1-Score unitless · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourcescGPT: Cell-type identification Native scLLM cell-type identification as reported in Table 2. Aggregation: Not reported Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Table 2, M.S. / scGPT row, F1-Score column |
| Configuration: Geneformer | Task: Cell-type identification Dataset: M.S. single-cell dataset | 0.388 F1-Score unitless · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceGeneformer: Cell-type identification Native scLLM cell-type identification as reported in Table 2. Aggregation: Not reported Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Table 2, M.S. / Geneformer row, F1-Score 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
M.S., Zheng68k, NSCLC and COVID-19 single-cell datasets. Original study splits and preprocessing are retained for reused benchmarks; validation loss selects checkpoints. Accuracy, precision, recall and weighted F1; silhouette is an additional embedding diagnostic. Prompt-based adaptation and traditional fine-tuning of single-cell models. The source states evaluated datasets were not used in the assessed models’ pretraining; an independent corpus audit remains outstanding. 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.
Sources
Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41Recorded 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
- Frozen-backbone prompting and full fine-tuning use different adaptation budgets. Exact donor grouping remain unextracted.
Sources
Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41
Profile review details
Task-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced.
Stable record: reported-task-5b929593eefc76Specifications
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 | M.S., Zheng68k, NSCLC and COVID-19 single-cell datasets.SourcesParameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 |
| Splits | Original study splits and preprocessing are retained for reused benchmarks; validation loss selects checkpoints.SourcesParameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 |
| Metrics | Accuracy, precision, recall and weighted F1; silhouette is an additional embedding diagnostic.SourcesParameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 |
| Baselines | Prompt-based adaptation and traditional fine-tuning of single-cell models.SourcesParameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 |
| Leakage controls | The source states evaluated datasets were not used in the assessed models’ pretraining; an independent corpus audit remains outstanding.SourcesParameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 |
| 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 sourcesSourcesParameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 |
| Entity type | Paper-specific computational evaluation protocol.SourcesParameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 |
| Organisms | Dataset-specific cell collections including human disease datasets.SourcesParameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 |
| Assays | Single-cell expression and cell-type annotations.SourcesParameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 |
| Allowed inputs | Single-cell gene-expression representations.SourcesParameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 |
| Adaptation | Prompt-based parameter-efficient adaptation versus conventional model fine-tuning.SourcesParameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 |
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 |
|---|---|---|
| Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification | preprint archived 2024-01-30 | Read source |
Historical gaps recorded on 2026-09-17
The catalogue now holds 2 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
- Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification 10.1101/2024.01.27.577455
Evidence locations
- Table 3.; XML table T3
- Table 2.; XML table T2
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 | Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 Version: preprint archived 2024-01-30 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Diagram steps
| Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 Version: preprint archived 2024-01-30 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Computational evaluation flow Individual claims | Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 Version: preprint archived 2024-01-30 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Datasets M.S., Zheng68k, NSCLC and COVID-19 single-cell datasets. Individual claims | Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 Version: preprint archived 2024-01-30 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Splits Original study splits and preprocessing are retained for reused benchmarks; validation loss selects checkpoints. Individual claims | Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 Version: preprint archived 2024-01-30 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Adaptation Prompt-based parameter-efficient adaptation versus conventional model fine-tuning. Individual claims | Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 Version: preprint archived 2024-01-30 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Metrics Accuracy, precision, recall and weighted F1; silhouette is an additional embedding diagnostic. Individual claims | Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 Version: preprint archived 2024-01-30 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Baselines Prompt-based adaptation and traditional fine-tuning of single-cell models. Individual claims | Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 Version: preprint archived 2024-01-30 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Leakage controls The source states evaluated datasets were not used in the assessed models’ pretraining; an independent corpus audit remains outstanding. Individual claims | Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 Version: preprint archived 2024-01-30 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. 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 | Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41 Version: preprint archived 2024-01-30 | unreported automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. 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
- Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Original source · preprint archived 2024-01-30
- Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification · Original source · preprint archived 2024-01-30
Technical metadata and extraction receipts
Stable ID: reported-task-5b929593eefc76
- areas
- cells-tissues
- tasks
- Cell-type identification
- entity level
- task
- version
- Not reported
- task
- Cell-type identification
- 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-single-cell-peft-2024-77a4a859; inspected locators: Table 3.; XML table T3; Table 2.; XML table T2; searched queries: Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification 10.1101/2024.01.27.577455; 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; split: 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: single-cell-peft-2024; source locator: Methods: Finetuning and evaluation settings; Data preparation; cached text lines 36–41; 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: M.S. single-cell dataset
- benchmark: scGPT: Cell-type identification
- benchmark: Geneformer: Cell-type identification