{"id":"VC0145","type":"paper","title":"A comparison of computational methods for expression forecasting.","authors_or_org":"Kernfeld E, Yang Y, Weinstock JS, Battle A, Cahan P.","date":"2025-11-17","year":"2025","venue":"Genome biology","url":"https://doi.org/10.1186/s13059-025-03840-y","doi":"10.1186/s13059-025-03840-y","pmid":"41250104","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":"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.","one_line_summary_zh":"Kernfeld 等（Genome Biology 2025）：PEREGGRN 基准含 11 个扰动数据集；报告表达预测方法很少优于简单基线，并以偏差-方差和刻板性扰动响应两种假说讨论训练集均值为何强。","verified":"yes","verification_source":"Europe PMC (metadata, abstract) + Europe PMC full-text XML read 2026-10-16 (PMC12621394)","page_url":"https://gliostate.com/library/VC0145/","api_url":"https://gliostate.com/api/items/VC0145.json","sameAs":["https://doi.org/10.1186/s13059-025-03840-y","https://pubmed.ncbi.nlm.nih.gov/41250104/"],"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":"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"},{"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"}]}