scVI: uses model scVI
scVI batch correction followed by a trained MLP predicts drug response. Current configuration label should be clarified as a pipeline; relation uses_model avoids importing it as standalone scVI performance.
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.
6 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| attributes.field links:uses_model:catalog-model-scvi Context-only references | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Methods / Comparison experiments at individual cell level; scVI comparator paragraph Version: PMC archival version PMC12859067.1 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.source_locator Methods / Comparison experiments at individual cell level; scVI comparator paragraph Context-only references | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Methods / Comparison experiments at individual cell level; scVI comparator paragraph Version: PMC archival version PMC12859067.1 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.target_id catalog-model-scvi Context-only references | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Methods / Comparison experiments at individual cell level; scVI comparator paragraph Version: PMC archival version PMC12859067.1 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| description scVI batch correction followed by a trained MLP predicts drug response. Current configuration label should be clarified as a pipeline; relation uses_model avoids importing it as standalone scVI performance. Context-only references | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Methods / Comparison experiments at individual cell level; scVI comparator paragraph Version: PMC archival version PMC12859067.1 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Relationship: subject reported-model-f23306b94dc7b6 Context-only references | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Methods / Comparison experiments at individual cell level; scVI comparator paragraph Version: PMC archival version PMC12859067.1 | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| name scVI: uses model scVI Context-only references | scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning Methods / Comparison experiments at individual cell level; scVI comparator paragraph Version: PMC archival version PMC12859067.1 | not individually reviewed No individual claim review recorded Audit detailsField: 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: source checked
1 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
Technical metadata and extraction receipts
Stable ID: model-evaluation-identity-b94b470742757bb69977
- areas
- cells-tissues
- field
- links:uses_model:catalog-model-scvi
- target id
- catalog-model-scvi
- source locator
- Methods / Comparison experiments at individual cell level; scVI comparator paragraph
- review
- method: automated_source_review; date: 2026-09-23; note: Source review establishes this relationship only. Exact evaluated configurations and original numerical review status remain unchanged. scVI batch correction followed by a trained MLP predicts drug response. Current configuration label should be clarified as a pipeline; relation uses_model avoids importing it as standalone scVI performance.
Related records
- subject: scVI