preprint✓ VerifiedVC0179
Liang W-L, Yang Y-B, Zhang Q-Y, Feng J-C, Wang Q-F, Zou D, Tang J-L, Liu Y, Wang S-Y, Duan J, Bai Y, Li Y-F, Sun W-Y, Cao Y-F, He R-R. · bioRxiv (preprint, not peer-reviewed) · 2026-10-13
Preprint (not peer-reviewed): Liang et al. (Macau University of Science and Technology; Jinan University) show that conventional evaluation of few-shot adaptation in single-cell perturbation prediction shares one control estimate between calibration responses and held-out targets, which carries control error into predictions and inflates scores. Scoring against an independent control changed the rank of 40 of 52 configuration–dataset entries. They introduce SafeDelta, a split-control criterion that uses calibration data to decide whether to adapt; across six datasets, mean gated gain was non-negative under the original two-control evaluation.
DOI
paper✓ VerifiedVC0145
Kernfeld E, Yang Y, Weinstock JS, Battle A, Cahan P. · Genome biology · 2025-11-17
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.
DOIPubMed
paper✓ VerifiedVC0144
Wong DR, Hill AS, Moccia R. · Bioinformatics · 2025-06-01
Wong, Hill, Moccia (Bioinformatics 2025): a 'CRISPR-informed mean' (training-set perturbed-cell mean, with the target gene set by prior knowledge of CRISPRi/CRISPRa) is reported to beat fine-tuned scGPT, GEARS and simple neural baselines on Adamson, Norman and Replogle K562 Essential; also provides a corrected Adamson dataset.
DOIPubMed
paper✓ VerifiedVC0146
Miller HE, Mejia GM, Leblanc FJA, Swain B, Wang B, de Lima Camillo LP. · Nature biotechnology · 2026-10-01
Miller et al. (Nature Biotechnology 2026; authors from Shift Bioscience, competing-interest statement in paper): positive-control baselines and a 'dynamic range fraction' calibration framework over 14 datasets and 18 metrics; argues MSE and Pearson delta are often miscalibrated and that deep-learning models can beat uninformative baselines on calibrated metrics. Counter-position to VC0048/VC0049/VC0052/VC0144/VC0145.
DOIPubMed
paper✓ VerifiedVC0048
Ahlmann-Eltze C, Huber W, Anders S. · Nature methods · 2025-08-04
Ahlmann-Eltze, Huber, Anders: deep models don't beat simple linear baselines for perturbation prediction.
DOIPubMed
paper✓ VerifiedVC0049
Csendes G, Sanz G, Szalay KZ, Szalai B. · BMC genomics · 2025-04-23
Csendes et al.: foundation cell models vs simple baselines for post-perturbation prediction.
DOIPubMed