An AI virtual cell is a model intended to simulate how cell states change under perturbations. Many general models exist, but GBM-specific applications are few and early, and benchmarks show that perturbation predictions often do not beat simple baselines.
VC0071 · preprint A Proteome-Calibrated Mechanistic Digital Twin of the Glioblastoma Astrocyte Derived from Patient Multi-Omic Data — Mayfield JD., bioRxiv, 2026
VC0033 · preprint X-Cell: Scaling Causal Perturbation Prediction Across Diverse Cellular Contexts via Diffusion Language Models — Wang C et al., bioRxiv, 2026
VC0132 Drug and single-cell gene expression integration identifies sensitive and resistant glioblastoma cell populations — Suter et al., Nature Communications, 2026
VC0129 · preprint DELPHAI, AI Agent for Predicting Drug Response and Resistance — Peng et al., preprint, 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
VC0001 How to build the virtual cell with artificial intelligence: Priorities and opportunities. — Bunne C et al., Cell, 2024
VC0011 Large-scale foundation model on single-cell transcriptomics. — Hao M, Gong J, Zeng X, Liu C, Guo Y, Cheng X, Wang T, Ma J, Zhang X, Song L., Nature methods, 2024
VC0008 scGPT: toward building a foundation model for single-cell multi-omics using generative AI. — Cui H, Wang C, Maan H, Pang K, Luo F, Duan N, Wang B., Nature methods, 2024
VC0013 Predicting transcriptional outcomes of novel multigene perturbations with GEARS. — Roohani Y, Huang K, Leskovec J., Nature biotechnology, 2023
VC0009 Transfer learning enables predictions in network biology. — Theodoris CV, Xiao L, Chopra A, Chaffin MD, Al Sayed ZR, Hill MC, Mantineo H, Brydon EM, Zeng Z, Liu XS, Ellinor PT., Nature, 2023
VC0014 Predicting cellular responses to complex perturbations in high-throughput screens. — Lotfollahi M et al., Molecular systems biology, 2023
VC0040 Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. — Replogle JM et al., Cell, 2022