{"scope":{"layer":"general","tissue_scope":"unknown","species":"unknown","system":"unknown","evidence_level":"preprint","importance":"","importance_reason":""},"id":"VC0179","type":"preprint","title":"A split-control criterion for few-shot adaptation in single-cell perturbation prediction","authors_or_org":"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S-Y, Duan J, Bai Y, Li Y-F, Sun W-Y, Cao Y-F, He R-R.","date":"2026-10-13","year":"2026","venue":"bioRxiv (preprint, not peer-reviewed)","url":"https://doi.org/10.64898/2026.10.05.756906","doi":"10.64898/2026.10.05.756906","pmid":"","brain_tumor_focus":"no","tags":["perturbation","benchmark","critique"],"tag_slugs":["perturbation","benchmark","critique"],"tag_labels":["Perturbation","Benchmark & validation","Critique"],"tag_labels_zh":["扰动","基准评测与验证","批评与反证"],"unmapped_tags":[],"one_line_summary_en":"Preprint (not peer-reviewed): Liang et al.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.","one_line_summary_zh":"预印本（未经同行评审）：Liang 等（澳门科技大学、暨南大学）指出，单细胞扰动预测中少样本适配的常规评测让校准响应与留出目标共用同一个对照估计，会把对照误差带入预测并虚高得分。改用独立对照评分后，52 个“配置–数据集”条目中有 40 个排名发生变化。作者提出 SafeDelta，一种用校准数据决定是否进行适配的分离对照准则；在 6 个数据集上，按原有双对照评测，门控后的平均增益不为负。","verified":"yes","verification_source":"bioRxiv API details (title, 15 authors, posted 2026-10-13, v1, new results, bioinformatics, CC BY-NC-ND, Macau University of Science and Technology / Jinan University, abstract naming SafeDelta) fetched 2026-10-20; full text not read","page_url":"https://gliostate.com/library/VC0179/","api_url":"https://gliostate.com/api/items/VC0179.json","sameAs":["https://doi.org/10.64898/2026.10.05.756906"],"is_preprint":true,"facets":{"type":"https://gliostate.com/library/type/preprint/","year":"https://gliostate.com/library/year/2026/","tags":["https://gliostate.com/library/tag/perturbation/","https://gliostate.com/library/tag/benchmark/","https://gliostate.com/library/tag/critique/"]},"cited_by":[],"related":[{"id":"VC0180","title":"Transcriptional perturbation effects remain structured beyond the leading global trend","shared_tags":["perturbation","benchmark","critique"],"page_url":"https://gliostate.com/library/VC0180/","api_url":"https://gliostate.com/api/items/VC0180.json"},{"id":"VC0145","title":"A comparison of computational methods for expression forecasting.","shared_tags":["benchmark","critique","perturbation"],"page_url":"https://gliostate.com/library/VC0145/","api_url":"https://gliostate.com/api/items/VC0145.json"},{"id":"VC0144","title":"Simple controls exceed best deep learning algorithms and reveal foundation model effectiveness for predicting genetic perturbations.","shared_tags":["benchmark","critique","perturbation"],"page_url":"https://gliostate.com/library/VC0144/","api_url":"https://gliostate.com/api/items/VC0144.json"},{"id":"VC0146","title":"Deep learning perturbation models can outperform baselines on calibrated metrics.","shared_tags":["benchmark","critique"],"page_url":"https://gliostate.com/library/VC0146/","api_url":"https://gliostate.com/api/items/VC0146.json"},{"id":"VC0048","title":"Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines.","shared_tags":["benchmark","critique"],"page_url":"https://gliostate.com/library/VC0048/","api_url":"https://gliostate.com/api/items/VC0048.json"},{"id":"VC0049","title":"Benchmarking foundation cell models for post-perturbation RNA-seq prediction.","shared_tags":["benchmark","critique"],"page_url":"https://gliostate.com/library/VC0049/","api_url":"https://gliostate.com/api/items/VC0049.json"}]}