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A comparison of computational methods for expression forecasting.

Kernfeld et al. (Genome Biology 2025): PEREGGRN benchmark of 11 perturbation datasets; reports it is uncommon for expression-forecasting methods to beat simple baselines, and discusses a bias-variance explanation and stereotypical perturbation responses for the strength of the training-data mean.

Authors / organisation: Kernfeld E, Yang Y, Weinstock JS, Battle A, Cahan P.
Venue: Genome biology
Date: 2025-11-17
Type: paper
Year: 2025
DOI: 10.1186/s13059-025-03840-y
PMID: 41250104
Verification: Europe PMC (metadata, abstract) + Europe PMC full-text XML read 2026-10-16 (PMC12621394)

Benchmark & validation Critique Perturbation

Cited by GlioState topics

Linear baselines and systematic variation in perturbation prediction: what 'a strong baseline' means, and where the papers disagree (Validation)

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A comparison of computational methods for expression forecasting.

Cited by GlioState topics

Entities mentioned

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