{"id":"VC0144","type":"paper","title":"Simple controls exceed best deep learning algorithms and reveal foundation model effectiveness for predicting genetic perturbations.","authors_or_org":"Wong DR, Hill AS, Moccia R.","date":"2025-06-01","year":"2025","venue":"Bioinformatics","url":"https://doi.org/10.1093/bioinformatics/btaf317","doi":"10.1093/bioinformatics/btaf317","pmid":"40407144","brain_tumor_focus":"no","tags":["benchmark","critique","linear-baseline","perturbation"],"tag_slugs":["benchmark","critique","perturbation"],"tag_labels":["Benchmark & validation","Critique","Perturbation"],"tag_labels_zh":["基准评测与验证","批评与反证","扰动"],"unmapped_tags":[],"one_line_summary_en":"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.","one_line_summary_zh":"Wong、Hill、Moccia（Bioinformatics 2025）：报告“CRISPR 先验均值”（训练集扰动细胞均值，靶基因按 CRISPRi/CRISPRa 先验设定）在 Adamson、Norman、Replogle K562 Essential 上优于微调 scGPT、GEARS 及简单神经基线；并提供修正后的 Adamson 数据集。","verified":"yes","verification_source":"Europe PMC (metadata, abstract) + Europe PMC full-text XML read 2026-10-16 (PMC12202205)","page_url":"https://gliostate.com/library/VC0144/","api_url":"https://gliostate.com/api/items/VC0144.json","sameAs":["https://doi.org/10.1093/bioinformatics/btaf317","https://pubmed.ncbi.nlm.nih.gov/40407144/"],"is_preprint":false,"facets":{"type":"https://gliostate.com/library/type/paper/","year":"https://gliostate.com/library/year/2025/","tags":["https://gliostate.com/library/tag/benchmark/","https://gliostate.com/library/tag/critique/","https://gliostate.com/library/tag/perturbation/"]},"cited_by":[{"id":"validation-linear-baselines-systematic-variation","type":"validation","title_en":"Linear baselines and systematic variation in perturbation prediction: what 'a strong baseline' means, and where the papers disagree","title_zh":"扰动预测中的线性基线与系统性变异：“强基线”指什么，各论文在哪里分歧","url":"https://gliostate.com/validation/linear-baselines-systematic-variation/","api_url":"https://gliostate.com/api/topics/validation-linear-baselines-systematic-variation.json"}],"related":[{"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":"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"},{"id":"VC0050","title":"Zero-shot evaluation reveals limitations of single-cell foundation models.","shared_tags":["benchmark","critique"],"page_url":"https://gliostate.com/library/VC0050/","api_url":"https://gliostate.com/api/items/VC0050.json"},{"id":"VC0078","title":"Biological domain shift and statistical nesting concerns in generative AI-based spatial tumor growth prediction for pediatric diffuse midline glioma.","shared_tags":["critique"],"page_url":"https://gliostate.com/library/VC0078/","api_url":"https://gliostate.com/api/items/VC0078.json"}]}