rewirebio.iobenchmarks
Configuration

GEARS (trained from scratch, no pretrained weights)

GEARS configuration as evaluated in PertEval-scFM Table 1, Norman single-gene section. The source reports modifying only the train-test splits (to align with SPECTRA) relative to the original GEARS implementation; other parameters are at their default values. Trained from scratch, without pretrained weights.

1 evaluation · 1 result

Overview

GEARS configuration as evaluated in PertEval-scFM Table 1, Norman single-gene section. The source reports modifying only the train-test splits (to align with SPECTRA) relative to the original GEARS implementation; other parameters are at their default values. Trained from scratch, without pretrained weights.

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Evaluations and results

1 evaluation · 1 result. Different protocols are not a single leaderboard.

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Exact evaluated configurations and original reported results
Tested configurationProtocol and datasetFindingEvidence and details
Configuration: GEARS (trained from scratch, no pretrained weights)Protocol: PertEval-scFM Norman single-gene (2,000 HVGs) AUSPC across SPECTRA sparsification splits
Dataset subset: Norman et al. 2019 single-gene perturbations, K562, top 2,000 HVGs (PertEval-scFM Table 1)
0.815 ± 0.039 AUSPC
10^-2 (printed column header units) · lower

Uncertainty: type: author_reported_propagated_standard_error; reported spread: 0.039; note: The source describes this quantity as a standard error (main-text Figure 2 caption: 'Average AUSPC (down-arrow) across sparsification probabilities for each model with standard error bars') and separately gives its own propagation formula (Appendix F.2, Eqs. F3-F5): AUSPC's uncertainty is derived from each split's own MSE uncertainty via the trapezoidal integral's partial derivatives (sigma^2 = sum_i (d/2)^2 * sigma_phi_i^2, where d=0.1 is the fixed sparsification step size). These two author statements describe the same quantity and are not in conflict: a propagated quantity can correctly be reported as a standard error. This is recorded as the author's own reported, propagated uncertainty; the F3-F5 derivation is the authors' own formula and its mathematical correctness has not been independently validated here. It must not be read as an independently resampled model-seed standard deviation or a confidence interval. The underlying per-split uncertainty is attributed by the source to triplicate experiments per model (Appendix I, Figure I1 caption: 'Experiments were carried out in triplicate for each model'), not to the main-text Figure 2 region. Figure I1's own caption separately states '8 train-test splits of increasing difficulty' for this same Norman single-gene evaluation, while Table 1 prints seven S-columns (S0.1-S0.7) and Appendix F.2 describes the sparsification probabilities as spanning 0.1 to 0.7. This 7-vs-8 discrepancy between the Figure I1 caption and the Table 1 / F.2 grid is preserved exactly as printed, not resolved; it must not be read as establishing an eighth Table 1 column or a confirmed n_runs=7, and no significance claim is made from any overlapping error bars.

Coverage: Not reported scored / Not reported eligible

Author-reported evaluation · Source checked
Methods, coverage and source

GEARS on PertEval-scFM Norman single-gene (2,000 HVGs) AUSPC

Trapezoidal-rule AUSPC of MSE, scored on the perturbation-effect delta=P-Xc (Eq. 5), across seven SPECTRA sparsification splits (s=0.1..0.7), Norman single-gene, 2,000 HVGs.

Aggregation: Not reported

PertEval-scFM (Wenteler et al., ICML 2025), full text · Table 1, Norman single-gene section, row GEARS, column AUSPC (10^-2).

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Release 2026-10-07-1448159e6a81 · Record review: source checked

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Stable ID: perteval-scfm-2025-method-gears

areas
cells-spatial-multiomics
source locator
Table 1, row GEARS, Norman single-gene section; Section 2.2 ('GEARS baseline', and the sentence 'we train GEARS from scratch without using pretrained weights').
missing metadata
checkpoint revision: unknown/unpublished: 'trained from scratch, no pretrained weights' means no pretrained starting checkpoint was used -- it does not mean no final trained artifact exists. The source does not state a published checkpoint/artifact identifier for the resulting trained model; the final run's artifact ID is simply not given, not inapplicable.; hyperparameters: official GEARS implementation defaults, apart from the SPECTRA-defined train-test split; exact values not separately tabulated in the main text
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