Linear perturbation model (Ahlmann-Eltze et al. 2025)
Bilinear model Y = G W P^T + b, with read-out gene embedding G and perturbation embedding P from a PCA of the training data or from an external source, W fitted by ridge-regularised least squares and b the training row means.
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- Linear model (G and P from training data) (Ahlmann-Eltze et al. 2025) Configuration · configuration · 3 evaluations · 6 results
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Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
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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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Stable ID: perturbation-ahlmann-20261011-method-linear-model
- areas
- cells-tissues; single-cell
- contexts
- research
- method types
- conventional_pipeline
- entity level
- method
- reported name
- linear model
- source locator
- Main text paragraph 14, equation (1); Methods 'Single perturbation benchmark setup' paragraphs 2 and 3
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- version: reason: inapplicable; note: Family record; settings are on the configuration