Strengths and considerations
No source-reviewed explanatory claims are recorded here yet.
Task
Gene-regulatory link prediction
Gene-regulatory link prediction uses cell-specific reference networks to evaluate inferred regulatory edges.
Sources
Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraphAt a glance
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 | Seven BEELINE human/mouse scRNA-seq datasets with cell-type-specific ChIP-seq reference networks.SourcesPrediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph |
| Splits | For each TF, target/non-target edges are divided 67:33 into training/test, with part of training reserved for validation; all TFs contribute to both arms.SourcesPrediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph |
| Metrics | AUROC and AUPRC; selected comparisons average results from two gene-panel sizes.SourcesPrediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph |
| Baselines | GNNLink, GENELink, GNE, CNNC, DeepDRIM, GRN-transformer, PCC, GRNBoost2 and GENIE3.SourcesPrediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph |
| Leakage controls | Target genes are separated between training and testing for the same TF. This is not a leave-TF-out design and does not establish globally disjoint gene identities across all TFs.SourcesPrediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph |
| 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 sourcesSourcesPrediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph |
| Entity type | Paper-specific computational evaluation protocol.SourcesPrediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph |
| Organisms | Human and mouse.SourcesPrediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph |
| Assays | Single-cell RNA-seq with cell-type-specific ChIP-seq regulatory references.SourcesPrediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph |
| Allowed inputs | Single-cell expression and candidate regulatory gene pairs.SourcesPrediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph |
| Adaptation | Supervised regulatory-link prediction compared with neural and conventional network-inference methods.SourcesPrediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph |
How it works
How it worksComputational evaluation flow
Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol.
Sources
Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraphEvaluation methodology
Seven BEELINE human/mouse scRNA-seq datasets with cell-type-specific ChIP-seq reference networks. For each TF, target/non-target edges are divided 67:33 into training/test, with part of training reserved for validation; all TFs contribute to both arms. AUROC and AUPRC; selected comparisons average results from two gene-panel sizes. GNNLink, GENELink, GNE, CNNC, DeepDRIM, GRN-transformer, PCC, GRNBoost2 and GENIE3. Target genes are separated between training and testing for the same TF. This is not a leave-TF-out design and does not establish globally disjoint gene identities across all TFs. 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
Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraphRecorded evaluations
Each evaluation records what was tested and under which conditions.
Tested entities and results
Release 2026-09-17-d277315f7d76 · 2 evaluations · 2 metric rows. Different protocols are not a single leaderboard.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| scRegNet (Geneformer backbone): Gene-regulatory link prediction Pipeline: scRegNet (Geneformer backbone)Task: Gene-regulatory link predictionDataset: hESC cell-type-specific GRN TFs plus 500 variable genes; mean from 50 independent evaluations. Author-reported evaluation · Evaluation metadata: needs review | ||
| 0.89 AUROC Unit: unitless · Direction: unknown | Uncertainty: ± 0.00 as printed Scored: Not reported · Eligible: Not reported | source checkedPrediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Table 2, scRegNet (w/ Geneformer) row, hESC AUROC entry Source checking is not independent reproduction. |
| scRegNet (scBERT backbone): Gene-regulatory link prediction Pipeline: scRegNet (scBERT backbone)Task: Gene-regulatory link predictionDataset: hESC cell-type-specific GRN TFs plus 500 variable genes; mean from 50 independent evaluations. Author-reported evaluation · Evaluation metadata: needs review | ||
| 0.88 AUROC Unit: unitless · Direction: unknown | Uncertainty: ± 0.00 as printed Scored: Not reported · Eligible: Not reported | source checkedPrediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Table 2, scRegNet (w/ scBERT) row, hESC AUROC entry Source checking is not independent reproduction. |
Papers and result coverage
Last literature check: 2026-09-17. Primary-source discovery and table/protocol screening; source checked is not independently reproduced. Raw acquisitions not automatically numerical publication approval.
| Paper or primary resource | Version | Reference |
|---|---|---|
| Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning | PMC11838224.2 | Read source DOI: 10.1101/2024.12.16.628715 |
What is still missing
- Raw XML stacked AUROC/AUPRC cells preserved with explicit linebreak delimiters. Two GENELink settings must not be conflated. Structured table extraction and exact uncertainty scope remain pending.
Search and extraction details
source found structured extraction pending
Searches
- Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning primary paper benchmark results
Evidence locations
- Tables2–3,500/1000genes and seven cell types; Methods
Strengths and limitations
Limitations and conditions
- Gene-panel definition and incomplete reference networks affect the candidate edge population. Negative-pair construction and cross-TF gene sharing must be distinguished from the stated per-TF target split.
Sources
Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph
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-3063ed4da76b4bEvidence 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 | Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph Version: PMC11838224.2 | 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 ["Input: Single-cell expression and candidate regulatory gene pairs.","Evaluation: Supervised regulatory-link prediction compared with neural and conventional network-inference methods.","Readout: AUROC and AUPRC; selected comparisons average results from two gene-panel sizes."] Individual claims | Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph Version: PMC11838224.2 | 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 | Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph Version: PMC11838224.2 | 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 Seven BEELINE human/mouse scRNA-seq datasets with cell-type-specific ChIP-seq reference networks. Individual claims | Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph Version: PMC11838224.2 | 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 For each TF, target/non-target edges are divided 67:33 into training/test, with part of training reserved for validation; all TFs contribute to both arms. Individual claims | Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph Version: PMC11838224.2 | 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 Supervised regulatory-link prediction compared with neural and conventional network-inference methods. Individual claims | Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph Version: PMC11838224.2 | 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 AUROC and AUPRC; selected comparisons average results from two gene-panel sizes. Individual claims | Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph Version: PMC11838224.2 | 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 GNNLink, GENELink, GNE, CNNC, DeepDRIM, GRN-transformer, PCC, GRNBoost2 and GENIE3. Individual claims | Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph Version: PMC11838224.2 | 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 Target genes are separated between training and testing for the same TF. This is not a leave-TF-out design and does not establish globally disjoint gene identities across all TFs. Individual claims | Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph Version: PMC11838224.2 | 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 | Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph Version: PMC11838224.2 | 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
Release 2026-09-17-d277315f7d76 · Record review: needs review
2 source records and release history
- Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Original source · PMC11838224.2
- Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning · Original source · PMC11838224.2
Technical metadata and extraction receipts
Stable ID: reported-task-3063ed4da76b4b
- areas
- cells-tissues
- tasks
- Gene-regulatory link prediction
- entity level
- task
- version
- Not reported
- task
- Gene-regulatory link prediction
- scope note
- Paper-specific evaluation task; protocol completeness requires further extraction.
- benchmark research
- review date: 2026-09-17; status: source_found_structured_extraction_pending; primary sources: evidence-expansion-p2-scregnet-2025-65b3272d47bb; inspected locators: Tables2–3,500/1000genes and seven cell types; Methods; searched queries: Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning primary paper benchmark results; gaps: Raw XML stacked AUROC/AUPRC cells preserved with explicit linebreak delimiters. Two GENELink settings must not be conflated. Structured table extraction and exact uncertainty scope remain pending.; claim scope: Primary-source discovery and table/protocol screening; source checked is not independently reproduced. Raw acquisitions not automatically numerical publication approval.
- 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: scregnet-2025; source locator: Methods: Datasets and data pre-processing; Baseline models and evaluation metrics; Results: ablations; cached text lines 46–48, 55–56, 65; benchmark datasets and per-TF partition paragraph; 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.