Model type
Study-specific predictive method; this record is the paper-specific evaluated configuration.
Configuration
scXDR
scXDR predicts single-cell drug responses through heterogeneous-network transfer learning.
Sources
scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Abstract (paragraph 2); Methods/Comparison experiments at the individual cell level (paragraph 2)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
scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Methods/Model construction (paragraph 4); Methods/Comparison experiments at the individual cell level (paragraph 2)Overview
Biological inputs
Single-cell expression profiles, drug/gene features and heterogeneous graph relationships
Sources
scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Abstract (paragraph 2); Methods/Comparison experiments at the individual cell level (paragraph 2)Outputs
Drug-response scores for cells
Sources
scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Methods/Case study (paragraph 4); Methods/Comparison experiments at the individual cell level (paragraph 3)Access
Official study implementation and usage documentation: https://github.com/QiGuan1920/scXDR2025/blob/5b39f37ba4df186eeea0881d59366458d2535db9/README.md. This pinned documentation revision is not automatically the evaluated weight revision.
Sources
QiGuan1920/scXDR2025 README.md · README.md; installation, model download and usage instructionslimited source coverage · Automated source review, 2026-09-16. All specifications and missing details
Evaluations and results
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: scXDR | Task: Cross-dataset single-cell drug response transfer Dataset: scXDR transfer scenario 2 | 0.825 AUC unitless · unknown Uncertainty: ± 0.1573 standard deviation Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourcescXDR: Cross-dataset single-cell drug response transfer Single-cell-to-single-cell transfer; source scenario 2. Aggregation: Not reported scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Table 2, scXDR row, Scenario 2 column |
Source checking is not independent reproduction. Release 2026-09-29-06401fd5b220.
Use this model
How it works, versions and access
How it works
How the evaluated method works
Drug, gene and cell nodes exchange messages; feature and structure alignment, reconstruction and drug–cell scoring support transfer between single-cell datasets.
Sources
scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Methods/Model construction (paragraph 4); Methods/Comparison experiments at the individual cell level (paragraph 2)What was evaluated
The linked evaluation record identifies scXDR: Cross-dataset single-cell drug response transfer. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-b3-017Strengths, limitations and unresolved questions
Strengths and limitations
Strengths and considerations
- Models cross-dataset transfer using single-cell information rather than simply assigning a bulk cell-line label to every cell.
Sources
scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Methods/Comparison experiments at the individual cell level (paragraph 2); Introduction (paragraph 4)
Limitations and conditions
- Response labels and cross-dataset assumptions remain important; a computational prediction is not an experimentally confirmed response or a clinical treatment recommendation.
Sources
scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Methods/Data collection and processing (paragraph 1); Methods/Case study (paragraph 5)
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-9f39de53f7a139Specifications
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 |
|---|---|
| Model type | Study-specific predictive method; this record is the paper-specific evaluated configuration.SourcesscXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Methods/Model construction (paragraph 4); Methods/Comparison experiments at the individual cell level (paragraph 2) |
| Architecture / procedure | Drug, gene and cell nodes exchange messages; feature and structure alignment, reconstruction and drug–cell scoring support transfer between single-cell datasets.SourcesscXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Methods/Model construction (paragraph 4); Methods/Comparison experiments at the individual cell level (paragraph 2) |
| Biological inputs | Single-cell expression profiles, drug/gene features and heterogeneous graph relationshipsSourcesscXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Abstract (paragraph 2); Methods/Comparison experiments at the individual cell level (paragraph 2) |
| Outputs | Drug-response scores for cellsSourcesscXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Methods/Case study (paragraph 4); Methods/Comparison experiments at the individual cell level (paragraph 3) |
| Parameters | An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning; QiGuan1920/scXDR2025 README.md · Results/Superior performance at the individual cell level compared to various methods; Results/Superior performance at the cell group level compared to various methods; Results/Contribution of model components and architecture to performance; Methods/Data collection and processing; Methods/Model construction; Methods/Experiment setting; Methods/Comparison experiments at the individual cell level; Methods/Comparison experiments at the cell group level; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision |
| Known versions / configuration | scXDR is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sourcesSourcesscXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. |
| Training data / fitting | Twelve GEO scRNA-seq datasets across six tumour types and ten drugs support 20 cross-dataset prediction tasks. Each task transfers from its specified source dataset to a separate target dataset.SourcesscXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Introduction (paragraph 4); Methods/Data collection and processing (paragraph 1) |
| Context limits | Cell features use 5,000 highly variable genes; drug and target features use molecular and protein descriptors.SourcesscXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Methods/Data collection and processing (paragraph 2); Abstract (paragraph 2) |
| Access | Official study implementation and usage documentation: https://github.com/QiGuan1920/scXDR2025/blob/5b39f37ba4df186eeea0881d59366458d2535db9/README.md. This pinned documentation revision is not automatically the evaluated weight revision.SourcesQiGuan1920/scXDR2025 README.md · README.md; installation, model download and usage instructions |
| Code licence | No explicit code licence was established from the paper’s availability statement and inspected repository-root documentation. · Not reported in inspected sourcesSourcesQiGuan1920/scXDR2025 README.md · README.md and repository-root licence-file search |
| 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 sourcesSourcesQiGuan1920/scXDR2025 README.md · README.md; checkpoint/access documentation and licence scope |
Evidence
Source checking verifies the cited claim or transcription. It does not establish independent reproduction.
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.
20 evidence rows matching the loaded filters
| 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 | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Methods/Model construction (paragraph 4); Methods/Comparison experiments at the individual cell level (paragraph 2) Version: PMC archival version PMC12859067.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Diagram steps
| scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Methods/Model construction (paragraph 4); Methods/Comparison experiments at the individual cell level (paragraph 2) Version: PMC archival version PMC12859067.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Evaluated procedure (conceptual) Individual claims | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Methods/Model construction (paragraph 4); Methods/Comparison experiments at the individual cell level (paragraph 2) Version: PMC archival version PMC12859067.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Model type Study-specific predictive method; this record is the paper-specific evaluated configuration. Individual claims | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Methods/Model construction (paragraph 4); Methods/Comparison experiments at the individual cell level (paragraph 2) Version: PMC archival version PMC12859067.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Architecture / procedure Drug, gene and cell nodes exchange messages; feature and structure alignment, reconstruction and drug–cell scoring support transfer between single-cell datasets. Individual claims | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Methods/Model construction (paragraph 4); Methods/Comparison experiments at the individual cell level (paragraph 2) Version: PMC archival version PMC12859067.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| 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 | QiGuan1920/scXDR2025 README.md README.md; checkpoint/access documentation and licence scope Version: 5b39f37ba4df186eeea0881d59366458d2535db9 | unreported automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Biological inputs Single-cell expression profiles, drug/gene features and heterogeneous graph relationships Individual claims | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Abstract (paragraph 2); Methods/Comparison experiments at the individual cell level (paragraph 2) Version: PMC archival version PMC12859067.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Outputs Drug-response scores for cells Individual claims | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Methods/Case study (paragraph 4); Methods/Comparison experiments at the individual cell level (paragraph 3) Version: PMC archival version PMC12859067.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | QiGuan1920/scXDR2025 README.md Results/Superior performance at the individual cell level compared to various methods; Results/Superior performance at the cell group level compared to various methods; Results/Contribution of model components and architecture to performance; Methods/Data collection and processing; Methods/Model construction; Methods/Experiment setting; Methods/Comparison experiments at the individual cell level; Methods/Comparison experiments at the cell group level; 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: 5b39f37ba4df186eeea0881d59366458d2535db9 | unreported automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Results/Superior performance at the individual cell level compared to various methods; Results/Superior performance at the cell group level compared to various methods; Results/Contribution of model components and architecture to performance; Methods/Data collection and processing; Methods/Model construction; Methods/Experiment setting; Methods/Comparison experiments at the individual cell level; Methods/Comparison experiments at the cell group level; 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: PMC archival version PMC12859067.1 | unreported automated source review · 2026-09-16 Audit detailsPrimary 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: 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
- scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Original source · PMC archival version PMC12859067.1
- QiGuan1920/scXDR2025 README.md · Original source · 5b39f37ba4df186eeea0881d59366458d2535db9
Technical metadata and extraction receipts
Stable ID: reported-model-9f39de53f7a139
- areas
- cells-tissues
- entity level
- method
- version
- Not reported
- reported name
- scXDR
- 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: scxdr-2026; source locator: Methods/Model construction (paragraph 4); Methods/Comparison experiments at the individual cell level (paragraph 2) | Abstract (paragraph 2); Methods/Comparison experiments at the individual cell level (paragraph 2); 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.