Model type
Probabilistic single-cell model; this record is the paper-specific evaluated configuration.
Configuration
scVI
This scVI-based comparator is evaluated in the scXDR single-cell drug-response study.
Sources
scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Abstract (paragraph 2); Methods/Experiment setting (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/Comparison experiments at the individual cell level (paragraph 2); Methods/Comparison experiments at the individual cell level (paragraph 1)At a glance
Biological inputs
Single-cell expression measurements
Sources
scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Methods/Comparison experiments at the individual cell level (paragraph 2); Methods/Comparison experiments at the cell group level (paragraph 3)Outputs
Latent representations and the associated response-prediction output
Sources
scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Results/Drug screening and drug response markers (paragraph 3); Results/Pan-cancer level drug clusters and tumor clusters (paragraph 2)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.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| scVI: Cross-dataset single-cell drug response transfer Configuration: scVITask: Cross-dataset single-cell drug response transferDataset: scXDR transfer scenario 2 Single-cell-to-single-cell transfer; source scenario 2. Independent external evaluation · Evaluation metadata: needs review | ||
| 0.6970 AUC Unit: unitless · Direction: unknown | Uncertainty: ± 0.2463 standard deviation Scored: Not reported · Eligible: Not reported | source checkedscXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Table 2, scVI row, Scenario 2 column Source checking is not independent reproduction. |
How it works
How the evaluated method works
A probabilistic variational-autoencoder representation of single-cell expression supports the paper’s downstream response-prediction comparison.
Sources
scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Methods/Comparison experiments at the individual cell level (paragraph 2); Methods/Comparison experiments at the individual cell level (paragraph 1)Underlying method and version boundaries
scvi-tools contains probabilistic models for single-cell analyses. scVI, scANVI and study-specific downstream heads are distinct procedures even when distributed through the same software package.
Sources
scverse/scvi-tools README.md · README.md; introduction, model description, pretrained-model and usage sections at pinned revisionWhat was evaluated
The linked evaluation record identifies scVI: 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-b3-018Strengths and limitations
Strengths and considerations
- Provides a conventional learned latent representation for comparison with heterogeneous graph transfer.
Sources
scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Methods/Comparison experiments at the individual cell level (paragraph 2); Methods/Model construction (paragraph 4)
Limitations and conditions
- The response prediction uses a study-specific downstream procedure; scVI alone is not a drug-response assay or validated therapeutic predictor.
Sources
scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Methods/Case study (paragraph 3); Methods/Comparison experiments at the individual cell level (paragraph 3)
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-f23306b94dc7b6Specifications
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 | Probabilistic single-cell model; this record is the paper-specific evaluated configuration.Sourcesscverse/scvi-tools README.md · README.md model description |
| Architecture / procedure | A probabilistic variational-autoencoder representation of single-cell expression supports the paper’s downstream response-prediction comparison.SourcesscXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Methods/Comparison experiments at the individual cell level (paragraph 2); Methods/Comparison experiments at the individual cell level (paragraph 1) |
| Biological inputs | Single-cell expression measurementsSourcesscXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Methods/Comparison experiments at the individual cell level (paragraph 2); Methods/Comparison experiments at the cell group level (paragraph 3) |
| Outputs | Latent representations and the associated response-prediction outputSourcesscXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Results/Drug screening and drug response markers (paragraph 3); Results/Pan-cancer level drug clusters and tumor clusters (paragraph 2) |
| 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; scverse/scvi-tools 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 | scVI 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 | scVI corrects batch effects across single-cell datasets; an additional MLP is then trained on the corrected representation to predict drug response. This evaluates the scVI-plus-MLP pipeline.SourcesscXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning · Methods / Comparison experiments at the individual cell level; scVI comparator paragraph |
| Context limits | A maximum input/context length 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; scverse/scvi-tools 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 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/scverse/scvi-tools/blob/73b28e44223621470e582a81a102c107bb22678b/README.md. This pinned documentation revision is not automatically the evaluated weight revision.Sourcesscverse/scvi-tools README.md · README.md; installation, model download and usage instructions |
| Code licence | BSD 3-Clause (upstream repository code at the cited revision; this does not establish every dependency or historical checkpoint licence).Sourcesscverse/scvi-tools 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 sourcesSourcesscverse/scvi-tools 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
| 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/Comparison experiments at the individual cell level (paragraph 2); Methods/Comparison experiments at the individual cell level (paragraph 1) 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 ["Single-cell expression measurements","scVI","Latent representations and the associated response-prediction output"] Individual claims | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Methods/Comparison experiments at the individual cell level (paragraph 2); Methods/Comparison experiments at the individual cell level (paragraph 1) 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/Comparison experiments at the individual cell level (paragraph 2); Methods/Comparison experiments at the individual cell level (paragraph 1) 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 Probabilistic single-cell model; this record is the paper-specific evaluated configuration. Individual claims | scverse/scvi-tools README.md README.md model description Version: 73b28e44223621470e582a81a102c107bb22678b | 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 A probabilistic variational-autoencoder representation of single-cell expression supports the paper’s downstream response-prediction comparison. Individual claims | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Methods/Comparison experiments at the individual cell level (paragraph 2); Methods/Comparison experiments at the individual cell level (paragraph 1) 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 | scverse/scvi-tools README.md README.md; checkpoint/access documentation and licence scope Version: 73b28e44223621470e582a81a102c107bb22678b | 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 measurements Individual claims | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Methods/Comparison experiments at the individual cell level (paragraph 2); Methods/Comparison experiments at the cell group 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 |
| Outputs Latent representations and the associated response-prediction output Individual claims | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Results/Drug screening and drug response markers (paragraph 3); Results/Pan-cancer level drug clusters and tumor clusters (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 |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | scverse/scvi-tools 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: 73b28e44223621470e582a81a102c107bb22678b | 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
Release 2026-09-17-d277315f7d76 · Record review: needs review
3 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
- scverse/scvi-tools README.md · Original source · 73b28e44223621470e582a81a102c107bb22678b
- scverse/scvi-tools LICENSE · Original source · 73b28e44223621470e582a81a102c107bb22678b
Technical metadata and extraction receipts
Stable ID: reported-model-f23306b94dc7b6
- areas
- cells-tissues
- entity level
- method
- version
- Not reported
- reported name
- scVI
- 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; evidence-reported-base-scvi-readme-md; source locator: Methods/Comparison experiments at the individual cell level (paragraph 2); Methods/Comparison experiments at the individual cell level (paragraph 1) | README.md model description | Abstract (paragraph 2); Methods/Experiment setting (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.