Validation currently relies on benchmarks and challenges. Several independent studies report that deep perturbation models do not yet reliably beat simple linear baselines; clinical validation of virtual cells in neuro-oncology has not been reported in the library.目前的验证主要依靠基准与挑战赛。多项独立研究报告深度扰动模型尚不能稳定超越简单线性基线；文献库中尚无神经肿瘤领域虚拟细胞的临床验证报告。
In a GlioState re-analysis of public data (GSE131928), tumor-level state composition replicated moderately across Smart-seq2 and 10X (Spearman ρ=0.82 over 24 tumor×state values in 6 shared tumors; dominant state matched in 4 of 6), and a classifier using no signature genes recovered signature-based state calls on held-out 10X cells with balanced accuracy 0.75 (chance 0.25; single-marker baseline 0.53). This tests the reproducibility of state definitions only; it is not a test of any therapy-response or virtual-cell model, and the labels are not ground truth.在 GlioState 对公开数据（GSE131928）的再分析中，肿瘤水平的状态构成在 Smart-seq2 与 10X 之间中等程度复现（6 个共有肿瘤、24 个肿瘤×状态值的 Spearman ρ=0.82；优势状态 6 例中 4 例一致）；不使用任何特征基因的分类器在留出的 10X 细胞上还原了基于特征基因的状态判定，平衡准确率 0.75（随机水平 0.25；单标志基因基线 0.53）。这只检验状态定义的可重复性，并非对任何治疗反应或虚拟细胞模型的检验，且标签不是金标准。
VC0078 Biological domain shift and statistical nesting concerns in generative AI-based spatial tumor growth prediction for pediatric diffuse midline glioma. — Li W, Sun X, Pei S., BMC medicine, 2026
VC0054 · preprint · 预印本 An Empirical Comparison of Virtual Cell Models: Perturbation Prediction, Representation, and the Baseline Gap — Denvis O., Research Square, 2026
VC0056 Systematic evaluation of single-cell foundation model interpretability: attention-derived edge scores add no incremental value over gene-level features for perturbation-target prediction. — Kendiukhov I., BMC genomics, 2026
VC0077 Generative AI for spatial tumor growth on MRI: a proof-of-principle study in pediatric diffuse midline glioma. — Laslo D et al., BMC medicine, 2026
VC0053 · preprint · 预印本 Signal, Bounds, and Baselines: Principles for Evaluating Virtual Cell Perturbation Models — Vollenweider M, Bühlmann P., bioRxiv, 2026
VC0033 · preprint · 预印本 X-Cell: Scaling Causal Perturbation Prediction Across Diverse Cellular Contexts via Diffusion Language Models — Wang C et al., bioRxiv, 2026
VC0060 Charting the single-cell and spatial landscape of IDH-wild-type glioblastoma with GBmap. — Ruiz-Moreno C et al., Neuro-oncology, 2025
VC0052 Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation. — Viñas Torné R, Wiatrak M, Piran Z, Fan S, Jiang L, Teichmann SA, Nitzan M, Brbić M., Nature biotechnology, 2025
VC0048 Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines. — Ahlmann-Eltze C, Huber W, Anders S., Nature methods, 2025
VC0002 Virtual Cell Challenge: Toward a Turing test for the virtual cell. — Roohani YH et al., Cell, 2025
VC0050 Zero-shot evaluation reveals limitations of single-cell foundation models. — Kedzierska KZ, Crawford L, Amini AP, Lu AX., Genome biology, 2025
VC0092 · preprint · 预印本 Mapping the spatial architecture of glioblastoma from core to edge delineates niche-specific tumor cell states and intercellular interactions — Khan SM et al., bioRxiv, 2025
VC0070 In vivo perturb-seq of cancer and microenvironment cells dissects oncologic drivers and radiotherapy responses in glioblastoma. — Liu SJ et al., Genome biology, 2024
VC0069 A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. — Wang L, Jung J, Babikir H, Shamardani K, Jain S, Feng X, Gupta N, Rosi S, Chang S, Raleigh D, Solomon D, Phillips JJ, Diaz AA., Nature cancer, 2022
VC0059 An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma. — Neftel C et al., Cell, 2019