Configuration

Additive (Ahlmann-Eltze et al. 2025)

Additive: Sum of the two single-perturbation log fold changes; uses no double-perturbation data.

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Evaluations and results

1 evaluation · 2 results. Different protocols are not a single leaderboard.

Protocol: Norman double perturbations: expression prediction for held-out double perturbations (Ahlmann-Eltze et al. 2025 Fig. 1a and Extended Data Fig. 2a) · Dataset: Norman et al. 2019 Perturb-seq, K562 CRISPRa single and double perturbations (scFoundation reprocessing, Ahlmann-Eltze et al. 2025)

Sorted by L2 distance (1,000 most highly expressed control genes; mean over held-out perturbations and splits) (lower is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	L2 distance (1,000 most highly expressed control genes; mean over held-out perturbations and splits)	Pearson delta (1,000 most highly expressed control genes; mean over held-out perturbations and splits)
Additive (Ahlmann-Eltze et al. 2025)Evaluation	2.77Computed by Rewire from the authors' source data	0.892Computed by Rewire from the authors' source data

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Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: Additive (Ahlmann-Eltze et al. 2025)	Protocol: Norman double perturbations: expression prediction for held-out double perturbations (Ahlmann-Eltze et al. 2025 Fig. 1a and Extended Data Fig. 2a) Dataset: Norman et al. 2019 Perturb-seq, K562 CRISPRa single and double perturbations (scFoundation reprocessing, Ahlmann-Eltze et al. 2025)	0.892 Pearson delta (1,000 most highly expressed control genes; mean over held-out perturbations and splits)Computed by Rewire from the authors' source data unitless · higher Uncertainty: Not reported by the source: No spread of the mean is stated; Rewire did not compute one Coverage: 310/310 perturbation-split rows scored	Author-reported evaluation · Source checked Methods, coverage and source Additive on Norman doubles (Ahlmann-Eltze et al. 2025) perturbation-ahlmann-20261011-protocol-norman-double Aggregation: Not reported Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines; Source Data Extended Data Fig. 2, Ahlmann-Eltze et al. 2025 · Source Data Extended Data Fig. 2 (MOESM6), sheet "Panel A", rows with dataset_name "norman_from_scfoundation" and method "additive_model", column "r2_delta"; plotted as the red line in Extended Data Fig. 2a
Configuration: Additive (Ahlmann-Eltze et al. 2025)	Protocol: Norman double perturbations: expression prediction for held-out double perturbations (Ahlmann-Eltze et al. 2025 Fig. 1a and Extended Data Fig. 2a) Dataset: Norman et al. 2019 Perturb-seq, K562 CRISPRa single and double perturbations (scFoundation reprocessing, Ahlmann-Eltze et al. 2025)	2.77 L2 distance (1,000 most highly expressed control genes; mean over held-out perturbations and splits)Computed by Rewire from the authors' source data unit-unreported · lower Uncertainty: Not reported by the source: No spread of the mean is stated; Rewire did not compute one Coverage: 310/310 perturbation-split rows scored	Author-reported evaluation · Source checked Methods, coverage and source Additive on Norman doubles (Ahlmann-Eltze et al. 2025) perturbation-ahlmann-20261011-protocol-norman-double Aggregation: Not reported Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines; Source Data Fig. 1, Ahlmann-Eltze et al. 2025 · Source Data Fig. 1 (MOESM3), sheet "Panel A", rows with dataset_name "norman_from_scfoundation" and method "additive_model", column "l2"; plotted as the red line in Fig. 1a

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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

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Technical metadata and extraction receipts

Stable ID: perturbation-ahlmann-20261011-config-additive

areas: cells-tissues; single-cell
contexts: research
method types: conventional_pipeline
reported name: Additive (Source Data method "additive_model")
source locator: Methods 'Double perturbation benchmark setup' paragraph 2; Source Data column method "additive_model"
foundation model eligible: false
parameters: Sum of the two single-perturbation log fold changes; uses no double-perturbation data.
access reuse: Prediction scripts for every model: github.com/const-ae/linear_perturbation_prediction-Paper (benchmark/src), archived on Zenodo; branch link, not pinned to a commit in the article.
missing metadata: version: reason: inapplicable; note: A computed baseline with no software version
provenance note: Additive as run by Ahlmann-Eltze et al. 2025 for perturbation-effect prediction.

Related records

configuration of: Additive baseline for double perturbations
system: Additive on Norman doubles (Ahlmann-Eltze et al. 2025)

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