Baseline: Assess models for genetic perturbation experiments
Baseline of Assess models for genetic perturbation experiments
Training mean, no change, additive and linear models
Baseline method for the use case, reviewed against its cited sources.
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.
18 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Claimed field baseline Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines perturbation-response-20261009-source-csendes2025: Abstract and Results 'Benchmarking of post-perturbation RNA-seq prediction methods' (Train Mean); perturbation-ahlmann-20261011-source-article: title and Results (no change, additive and linear baselines) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Claimed field baseline Context-only references | Benchmarking foundation cell models for post-perturbation RNA-seq prediction perturbation-response-20261009-source-csendes2025: Abstract and Results 'Benchmarking of post-perturbation RNA-seq prediction methods' (Train Mean); perturbation-ahlmann-20261011-source-article: title and Results (no change, additive and linear baselines) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: BMC Genomics 26:393, published 2025-04-23; PMC12016270 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Method ids4 values
| Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines perturbation-response-20261009-source-csendes2025: Abstract and Results 'Benchmarking of post-perturbation RNA-seq prediction methods' (Train Mean); perturbation-ahlmann-20261011-source-article: title and Results (no change, additive and linear baselines) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Method ids4 values
| Benchmarking foundation cell models for post-perturbation RNA-seq prediction perturbation-response-20261009-source-csendes2025: Abstract and Results 'Benchmarking of post-perturbation RNA-seq prediction methods' (Train Mean); perturbation-ahlmann-20261011-source-article: title and Results (no change, additive and linear baselines) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: BMC Genomics 26:393, published 2025-04-23; PMC12016270 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Pinned records3 values
| Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines perturbation-response-20261009-source-csendes2025: Abstract and Results 'Benchmarking of post-perturbation RNA-seq prediction methods' (Train Mean); perturbation-ahlmann-20261011-source-article: title and Results (no change, additive and linear baselines) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Pinned records3 values
| Benchmarking foundation cell models for post-perturbation RNA-seq prediction perturbation-response-20261009-source-csendes2025: Abstract and Results 'Benchmarking of post-perturbation RNA-seq prediction methods' (Train Mean); perturbation-ahlmann-20261011-source-article: title and Results (no change, additive and linear baselines) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: BMC Genomics 26:393, published 2025-04-23; PMC12016270 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Rationale There is no pre-machine-learning method for predicting expression responses; the established comparators are simple statistical controls: the training mean, no change, the sum of single-perturbation effects and a linear model. Benchmarks report that foundation models often do not beat them. Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines perturbation-response-20261009-source-csendes2025: Abstract and Results 'Benchmarking of post-perturbation RNA-seq prediction methods' (Train Mean); perturbation-ahlmann-20261011-source-article: title and Results (no change, additive and linear baselines) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Rationale There is no pre-machine-learning method for predicting expression responses; the established comparators are simple statistical controls: the training mean, no change, the sum of single-perturbation effects and a linear model. Benchmarks report that foundation models often do not beat them. Context-only references | Benchmarking foundation cell models for post-perturbation RNA-seq prediction perturbation-response-20261009-source-csendes2025: Abstract and Results 'Benchmarking of post-perturbation RNA-seq prediction methods' (Train Mean); perturbation-ahlmann-20261011-source-article: title and Results (no change, additive and linear baselines) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: BMC Genomics 26:393, published 2025-04-23; PMC12016270 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Source location perturbation-response-20261009-source-csendes2025: Abstract and Results 'Benchmarking of post-perturbation RNA-seq prediction methods' (Train Mean); perturbation-ahlmann-20261011-source-article: title and Results (no change, additive and linear baselines) Context-only references | Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines perturbation-response-20261009-source-csendes2025: Abstract and Results 'Benchmarking of post-perturbation RNA-seq prediction methods' (Train Mean); perturbation-ahlmann-20261011-source-article: title and Results (no change, additive and linear baselines) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Source location perturbation-response-20261009-source-csendes2025: Abstract and Results 'Benchmarking of post-perturbation RNA-seq prediction methods' (Train Mean); perturbation-ahlmann-20261011-source-article: title and Results (no change, additive and linear baselines) Context-only references | Benchmarking foundation cell models for post-perturbation RNA-seq prediction perturbation-response-20261009-source-csendes2025: Abstract and Results 'Benchmarking of post-perturbation RNA-seq prediction methods' (Train Mean); perturbation-ahlmann-20261011-source-article: title and Results (no change, additive and linear baselines) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: BMC Genomics 26:393, published 2025-04-23; PMC12016270 full-text XML | 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
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
2 source records and release history
- Benchmarking foundation cell models for post-perturbation RNA-seq prediction · Original source · BMC Genomics 26:393, published 2025-04-23; PMC12016270 full-text XML
- Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines · Original source · Nature Methods 22(8):1657, published online 2025-08-04; PMC12328236 full-text XML
Technical metadata and extraction receipts
Stable ID: use-case-baseline-genetic-perturbation-response
- field
- baseline
- value
- Training mean, no change, additive and linear models
- source locator
- perturbation-response-20261009-source-csendes2025: Abstract and Results 'Benchmarking of post-perturbation RNA-seq prediction methods' (Train Mean); perturbation-ahlmann-20261011-source-article: title and Results (no change, additive and linear baselines)
- method ids
- perturbation-response-20261009-method-train-mean; perturbation-ahlmann-20261011-method-no-change; perturbation-ahlmann-20261011-method-additive; perturbation-ahlmann-20261011-method-linear-model
- rationale
- There is no pre-machine-learning method for predicting expression responses; the established comparators are simple statistical controls: the training mean, no change, the sum of single-perturbation effects and a linear model. Benchmarks report that foundation models often do not beat them.
- review
- method: source-hash-verification; automated-source-review; reviewer: claude; reviewer note: Separate Claude review agent, independent of the agent that proposed the baseline; no human review claimed; reviewed at: 2026-10-12T18:00:00Z; note: Accepted as proposed. Sources re-retrieved and read; method records and their configuration links checked against the use case's active comparisons. Names the reference newer methods are compared with, not a recommendation. See docs/reviews/use-cases/baselines-2026-10-12.md.
- pins
- record id: perturbation-ahlmann-20261011-source-article; fields: status; attributes.artifact_sha256; sha256: 4cc432f631d9f7012a62ce39d0f9ffc6b889422623700e3bb0e10b33d63fef61; record id: perturbation-response-20261009-source-csendes2025; fields: status; attributes.artifact_sha256; sha256: c2428f051f88ca43c76b4d05a6b8e186f5a5ea3e728d21b796a01a196f8ff0aa; record id: use-case-genetic-perturbation-response; fields: status; name; description; facets; attributes.question; attributes.decision; attributes.inputs; attributes.output; attributes.exclusions; attributes.setting; attributes.clinical_scope; attributes.intended_users; sha256: 440f0f2e30cb7c828212bed728de2420ba8e13521280be58be7bdcdfe57ac973