Keystone: AI and Gene Regulation Across Scales
Keystone Symposia · Breckenridge, CO (to Oct 7) · 2027-10-04
Keystone AI & gene regulation meeting (dates per search summary; verify).Keystone AI与基因调控会议（日期待复核）。
Library文献库
All 139 items, newest first. Search and filter run entirely in your browser.全部 139 条条目，按时间倒序。搜索与筛选均在浏览器本地完成。
Download the full library as CSV or JSON. Each item has a stable anchor, e.g. /library/#VC0001, and its own page, e.g. /library/VC0001/. Also: browse by tag, type and year, knowledge graph, data dictionary.可下载完整文献库 CSV 或 JSON。每条条目均有固定锚点（如 /library/#VC0001）和独立页面（如 /library/VC0001/）。另见：按标签、类型、年份浏览、知识图谱、数据字典。
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AACR Annual Meeting 2027
AACR · Orlando, FL (to Apr 7) · 2027-04-02
AACR 2027, Apr 2-7, Orlando.AACR 2027：4月2-7日奥兰多。
Keystone: Single Cell Biology: Expanding Potential Across Diseases
Keystone Symposia · Banff, Canada (to Feb 18) · 2027-02-15
Keystone 2027 single-cell/spatial/perturbation meeting.Keystone 2027单细胞会议。
NeurIPS 2026 Workshop: Foundation Models for the Brain and Body
NeurIPS · NeurIPS 2026, Sydney · 2026-12-11
NeurIPS 2026 workshop on FMs for brain and body (Dec 11-12, Sydney).NeurIPS 2026脑与身体基础模型研讨会。
AgenticLS @ NeurIPS 2026 — Agentic AI for Biological Discovery
NeurIPS · NeurIPS 2026, Sydney · 2026-12-11
NeurIPS 2026 workshop on agentic AI for biology.NeurIPS 2026生物发现智能体研讨会。
SIMBIOCHEM II · NeurIPS 2026 Workshop on ML for Simulation
NeurIPS · NeurIPS 2026, Sydney · 2026-12-11
NeurIPS 2026 workshop on ML for biochemical simulation.NeurIPS 2026生物化学模拟研讨会。
SNO 2026 31st Annual Meeting
Society for Neuro-Oncology · Philadelphia, PA (to Nov 15) · 2026-11-12
SNO 2026, Nov 12-15, Philadelphia; SNO 2027 Nov 18-21 Phoenix.SNO 2026：11月12-15日费城；2027年11月18-21日凤凰城。
Engineered regulatory states resolve context-dependent genetics in macular degeneration
Metzner E, Guruvayurappan K, Southard KM, Ardy RC, Dey KK, Norman TM. · bioRxiv · 2026-10-12
Norman/Dey labs (MSKCC): CRISPRa Multiome Perturb-seq of 119 TFs + dual-guide Perturb-seq of 1,225 TF pairs in RPE-1; chromatin priming predicts emergent states; Mosaic (Transformer) predicts TF-pair interaction residuals (r=0.484) and nominated a validated triple-TF combination.Norman/Dey 实验室（MSKCC）：在 RPE-1 中对 119 个 TF 做 CRISPRa Multiome Perturb-seq、对 1,225 个 TF 对做双向导 Perturb-seq；染色质预激活可预测新兴状态；Mosaic（Transformer）预测 TF 对相互作用残差（r=0.484），并提名经实验验证的三 TF 组合。
A State-Resolved Single-Cell and Spatial Atlas Reveals IDH-Associated Differences in Malignant and Immune Architecture in Diffuse Glioma.
Zaman A et al. · MedComm · 2026-10-04
State-resolved sc+spatial atlas of IDH-associated differences in glioma.IDH相关胶质瘤状态分辨图谱。
Cited in:引用于： Neural-progenitor-like (NPC-like) and OPC-like glioma cell states神经前体样（NPC 样）与少突前体样（OPC 样）胶质瘤细胞状态
ISP 3 Platform powered by Geneformer: Framework for Cross-Species, Sequential, and Multi-Gene In Silico Perturbation Screens with Application to iPS Cell State Transitions
Sanaki Y, Shigenobu S, Niwa R. · bioRxiv · 2026-09-24
ISP3 platform: Geneformer-based sequential multi-gene in silico perturbation.ISP3平台：基于Geneformer的多基因序贯模拟扰动。
EANO 2026 (21st meeting)
European Association of Neuro-Oncology · Rome, Italy (to Sep 27) · 2026-09-24
EANO 2026 Sep 24-27, Rome (already passed).EANO 2026：9月24-27日罗马（已结束）。
Beyond the drug-centric view: Advancing AI virtual cell platforms for environmental perturbation modelling.
Ukaegbu D, Curean V, Bender A, Maertens A. · ALTEX · 2026-09-17
Perspective on AI virtual cell platforms for environmental perturbation modelling.观点：虚拟细胞用于环境暴露/毒理扰动建模。
Spatial and single-cell dissection of medulloblastoma identifies developmental hierarchies and a prognostic PPIA-BSG tumor-immune axis.
Chen M, Duan B, Zhang J, Daniels C, Hu C, Yuan J, Guo J, Taylor MD, Shi X, Zhang L, Li D, Sun T. · Journal of translational medicine · 2026-09-15
Spatial + single-cell dissection of medulloblastoma hierarchies (2026).髓母细胞瘤空间与单细胞层级（2026）。
An operational perturbation proteomics-based virtual cell model.
Sun R et al. · Nature · 2026-09-09
Nature 2026: operational perturbation-proteomics-based virtual cell model.Nature 2026：基于扰动蛋白质组的可操作虚拟细胞模型。
Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution.
Zhang J et al. · Cell · 2026-09-01
Tahoe-100M peer-reviewed (Cell 2026).Tahoe-100M正式发表于Cell 2026。
Cited in:引用于： Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR胶质母细胞瘤扰动数据：放疗、药物与 CRISPR
scBaseCount: An AI agent-curated, standardized, auto-updated single-cell data repository.
Youngblut ND et al. · Cell · 2026-09-01
scBaseCount: AI-agent-curated, auto-updated single-cell repository (Arc).scBaseCount：AI代理自动整理的单细胞数据库（Arc）。
A world model of the virtual cell
Xing et al. · Cell · 2026-09
Cell peer-reviewed version of VCWM: persistent multimodal virtual-cell world model vs task-specific predictors.Cell正式版VCWM：持久多模态虚拟细胞世界模型，区别于任务型预测基础模型。
Cited in:引用于： AI virtual cell modelsAI 虚拟细胞模型
Predicting cellular responses to perturbation across diverse contexts with State.
Adduri AK et al. · Cell · 2026-08-31
State (Arc Institute): state-embedding + transition model across 70 contexts; Cell 2026.Arc的State模型：跨70种细胞情境预测扰动反应（Cell 2026）。
Cited in:引用于： AI virtual cell modelsAI 虚拟细胞模型 · Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR胶质母细胞瘤扰动数据：放疗、药物与 CRISPR · Validating virtual cells: benchmarks, baselines and critiques虚拟细胞的验证：基准、基线与批评
Virtual Cell Challenge 2026: Benchmarking zero-shot generalization across cellular contexts.
Arc Virtual Cell Initiative Team, Goodarzi H. · Cell · 2026-08-26
Virtual Cell Challenge 2026 paper: benchmarking zero-shot generalization across contexts.2026挑战赛论文：跨细胞情境零样本泛化基准。
Cited in:引用于： AI virtual cell modelsAI 虚拟细胞模型 · Validating virtual cells: benchmarks, baselines and critiques虚拟细胞的验证：基准、基线与批评
Virtual Cell Challenge 2026 (zero-shot, 6 unseen cell lines)
Arc Institute · virtualcellchallenge.org · 2026-08-20
2026 challenge: validation since Aug 20; final test set Oct 22; submissions due Nov 5 23:59 UTC; winners mid-late Nov (Arc official).2026挑战赛：8月20日起验证；10月22日发布终测集；11月5日23:59 UTC截止；11月中下旬公布（Arc官网确认）。
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 · 2026-08-12
Critique of the DMG generative growth study (domain shift, nesting).对上述DMG生成模型的方法学批评。
Cited in:引用于： Validating virtual cells: benchmarks, baselines and critiques虚拟细胞的验证：基准、基线与批评
PerturbLDM: conditional latent diffusion for modelling single-cell perturbation responses
Yu et al. · bioRxiv · 2026-08-12
PerturbLDM: Tahoe-100M-pretrained latent diffusion for conditional sc response generation across drug/dose/cell-line.PerturbLDM：在Tahoe-100M上预训练的条件潜扩散模型，生成药物/剂量/细胞系扰动的单细胞响应。
UniPert-G2CP bridges genetic and chemical screens from molecular representation to phenotype modeling
Li et al. · Cell · 2026-08
UniPert-G2CP: unified genetic/chemical perturbagen embeddings + CRISPR-to-drug phenotype transfer for AIVC.UniPert-G2CP：统一基因/化学扰动表征，并把CRISPR筛查迁移到药物表型预测。
Cited in:引用于： Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR胶质母细胞瘤扰动数据：放疗、药物与 CRISPR
Assessing scale and predictive diversity in models for single-cell transcriptomics based on Geneformer.
Chen J, Schmidt F, Henao R. · PLoS computational biology · 2026-07-30
Assessing scale and predictive diversity of Geneformer-based models.评估Geneformer类模型的规模效应。
Core2Edge: a human glioblastoma organoid-brain slice model capturing infiltration and transcriptional heterogeneity from core to single-cell dispersion.
Melhem A et al. · Nature protocols · 2026-07-28
Core2Edge: GBM organoid-brain slice infiltration model (Nat Protoc).Core2Edge：GBM类器官-脑片浸润模型。
Cited in:引用于： Glioblastoma single-cell and spatial datasets胶质母细胞瘤单细胞与空间数据集
An Empirical Comparison of Virtual Cell Models: Perturbation Prediction, Representation, and the Baseline Gap
Denvis O. · Research Square · 2026-07-22
Empirical comparison of virtual cell models: the baseline gap persists.虚拟细胞模型实证比较：与基线差距依旧。
Cited in:引用于： Validating virtual cells: benchmarks, baselines and critiques虚拟细胞的验证：基准、基线与批评
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 · 2026-07-22
Attention-derived edge scores in scFMs add no GRN information beyond baselines.单细胞基础模型注意力边得分未增加调控网络信息。
Cited in:引用于： Validating virtual cells: benchmarks, baselines and critiques虚拟细胞的验证：基准、基线与批评
Causal Models Need Causal Data - Xaira's X-Cell (podcast)
Latent Space · Latent Space podcast · 2026-07-21
Podcast: why causal (Perturb-seq) data and diffusion beat linear baselines.播客：因果数据与扩散模型。
ISMB 2026
ISCB · Washington, DC (to Jul 16) · 2026-07-12
ISMB 2026, Jul 12-16, Washington DC (from search summary).ISMB 2026：7月12-16日华盛顿。
ICML 2026 3rd Workshop on Multi-modal FMs & LLMs for Life Sciences
ICML · ICML 2026, COEX Seoul · 2026-07-11
ICML workshop incl. virtual-cell world model papers.ICML 2026生命科学多模态基础模型研讨会。
A World Model of the Virtual Cell (ICML 2026)
ICML 2026 · ICML 2026 · 2026-07
Proposes virtual cell world model (VCWM) as operational definition.提出“虚拟细胞世界模型”的操作性定义。
Lessons from Ron Alfa (Noetik) on AI-native precision oncology
Pear Healthcare Playbook · Substack · 2026-06-30
Interview on Noetik's virtual cell foundation models licensed to GSK.Noetik CEO访谈。
Single-cell and spatial transcriptomes reveal oligodendrocyte remodeling fatty acid metabolism microenvironment of lung cancer brain metastases.
Yang Y, Gao Y, Qi Z, Li S, Wang Z, Li D, Chen L, Zhou C, Feng M, Chen X, Hao B, Wu X, Li L, Cao Y. · Journal of translational medicine · 2026-06-16
Oligodendrocyte fatty-acid remodeling in brain metastasis (sc+spatial).脑转移中少突胶质细胞脂肪酸代谢重塑。
Automated and personalized glioblastoma tumor organoid drug screening platform exposes sensitivity to proteasome and HDAC inhibitors.
Jungwirth G et al. · NPJ precision oncology · 2026-06-16
Automated personalized GBM organoid drug screening platform.自动化个体化GBM类器官药筛平台。
Cited in:引用于： Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR胶质母细胞瘤扰动数据：放疗、药物与 CRISPR · Therapy-induced plasticity in glioblastoma胶质母细胞瘤的治疗诱导可塑性
Cellfm-datasets: A Unified Data Infrastructure for Single-Cell and Spatial Transcriptomics Foundation Model Pretraining
Zhang L, Pang J, Yan J, Tang W, Deng Y, He Y. · bioRxiv · 2026-06-14
CellFM-datasets: unified data infrastructure for single-cell/spatial.CellFM-datasets：统一单细胞与空间数据基础设施。
Generative AI for spatial tumor growth on MRI: a proof-of-principle study in pediatric diffuse midline glioma.
Laslo D et al. · BMC medicine · 2026-05-18
Generative AI forecasting spatial tumor growth on MRI in pediatric DMG.生成式AI预测儿童DMG的MRI空间生长。
Cited in:引用于： Validating virtual cells: benchmarks, baselines and critiques虚拟细胞的验证：基准、基线与批评
TumorTwin: a Python framework for patient-specific digital twins in oncology
Kapteyn et al. · BMC Med Inform Decis Mak · 2026-05-11
Open modular Python framework for patient-specific tumor digital twins; HGG growth/RT response demo.开源模块化Python肿瘤数字孪生框架；演示高级别胶质瘤生长与放疗反应。
Cited in:引用于： Therapy-induced plasticity in glioblastoma胶质母细胞瘤的治疗诱导可塑性
TxPert: using multiple knowledge graphs for prediction of transcriptomic perturbation effects.
Wenkel F et al. · Nature biotechnology · 2026-05-01
TxPert: multiple knowledge graphs for transcriptomic perturbation prediction.TxPert：多知识图谱预测转录组扰动效应。
LMRL Workshop @ ICLR 2026 (virtual cells)
ICLR · ICLR 2026, Rio de Janeiro · 2026-04-27
Learning Meaningful Representations of Life workshop focused on virtual cell bottlenecks.ICLR 2026 LMRL虚拟细胞研讨会。
Discovery of candidate therapeutic targets with Geneformer.
Zhang Y, Venkatesh MS, Theodoris CV. · Nature protocols · 2026-04-23
Nature Protocols: discovering therapeutic targets with Geneformer in silico perturbation.Nature Protocols：用Geneformer进行靶点发现的流程。
A Proteome-Calibrated Mechanistic Digital Twin of the Glioblastoma Astrocyte Derived from Patient Multi-Omic Data
Mayfield JD. · bioRxiv · 2026-04-23
Proteome-calibrated mechanistic digital twin of the GBM astrocyte (bioRxiv 2026).蛋白质组校准的GBM星形细胞机制性数字孪生（预印本）。
Cited in:引用于： AI virtual cell modelsAI 虚拟细胞模型
A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.
Sussman JH et al. · Cell reports. Medicine · 2026-04-23
Longitudinal single-cell & spatial atlas of pediatric high-grade glioma.儿童高级别胶质瘤纵向单细胞/空间图谱。
Cited in:引用于： Therapy-induced plasticity in glioblastoma胶质母细胞瘤的治疗诱导可塑性
Signal, Bounds, and Baselines: Principles for Evaluating Virtual Cell Perturbation Models
Vollenweider M, Bühlmann P. · bioRxiv · 2026-04-22
Signal, Bounds, and Baselines: principles for evaluating virtual cell perturbation models.虚拟细胞扰动模型评估原则：信号、上界与基线。
Cited in:引用于： Validating virtual cells: benchmarks, baselines and critiques虚拟细胞的验证：基准、基线与批评
Harnessing AI to Build Virtual Cells (VCHarness)
Cheng et al. · bioRxiv · 2026-04-14
VCHarness: autonomous AI coding agent that searches/refines perturbation-response model architectures for virtual cells.VCHarness：用自主AI编程智能体搜索并迭代扰动响应架构，加速虚拟细胞组件构建。
Scaling Bio 007: Xaira's Bo Wang and Ci Chu on building the virtual cell
Decoding Bio · Substack · 2026-03-30
Interview with Bo Wang & Ci Chu on X-Cell and generalization limits.王波、Ci Chu访谈：X-Cell与泛化局限。
X-Cell: Scaling Causal Perturbation Prediction Across Diverse Cellular Contexts via Diffusion Language Models
Wang C et al. · bioRxiv · 2026-03-20
X-Cell (Xaira): 4.9B-param diffusion model, perturbation scaling laws.Xaira的X-Cell：49亿参数扩散模型，显示扰动预测的缩放规律。
Cited in:引用于： AI virtual cell modelsAI 虚拟细胞模型 · Validating virtual cells: benchmarks, baselines and critiques虚拟细胞的验证：基准、基线与批评
AetherCell: A generative engine for virtual cell perturbation and in vivo drug discovery
Li et al. · bioRxiv · 2026-03-16
AetherCell unifies clinical RNA-seq and perturbation assays for generative response prediction and in vivo repurposing.AetherCell统一临床RNA-seq与扰动实验流形，做生成式响应预测并支持体内药物重定位。
Towards building a World Model to simulate perturbation-induced cellular dynamics by AlphaCell
Chuai et al. · bioRxiv · 2026-03-05
AlphaCell: genome-wide virtual-cell world model with OT conditional flow matching for cross-context perturbation dynamics.AlphaCell：全基因组虚拟细胞世界模型，用最优传输条件流匹配做跨语境扰动动力学。
Xaira's first virtual cell model is largest to-date
GEN · GEN · 2026-03
GEN on X-Cell; includes Noetik CEO Ron Alfa's tissue-first counterpoint.GEN报道X-Cell，并含Noetik观点。
Current landscape of single-cell genomics in meningioma.
Ellenbogen Y, Landry AP, Yefet LS, Ajisebutu A, Gui C, Persaud P, Kaloti R, Nassiri F. · Neuro-oncology advances · 2026-02-27
Review: current landscape of single-cell genomics in meningioma.综述：脑膜瘤单细胞基因组学现状。
Foundation Model Based on Routine Magnetic Resonance Imaging for Brain Tumor Molecular Profiling and Progression Prediction.
Li J, Liu R, Xing Y, Gao X, Yin Q, Su Q. · JCO precision oncology · 2026-02-20
MRI foundation model for brain tumor molecular profiling and progression.基于常规MRI的脑肿瘤分子分型基础模型。
Scalable probe-based single-cell transcriptional profiling for virtual cell perturbation mapping and synthetic biology phenotyping
Swinderman JT et al. · bioRxiv · 2026-02-05
Probe-based scalable single-cell profiling for virtual-cell perturbation mapping.探针法可扩展单细胞测序用于虚拟细胞扰动图谱。
CellFluxV2 : An Image Generative Foundation Model for Virtual Cell Modeling
Zhang Y, Su Y, Wefers Z, Su S, Li H, Li T, Wang C, Burgess J, Lozano A, Zhou L, Ding D, Nirschl J, Lundberg E, Yeung-Levy S. · bioRxiv · 2026-01-20
CellFluxV2: image generative foundation model for virtual cell modeling.CellFluxV2：面向虚拟细胞的图像生成基础模型。
Anatomically aware simulation of patient-specific glioblastoma xenografts
Malik et al. · PLoS Comput Biol · 2026-01-20
Patient-specific GBM xenograft growth/invasion model on mouse brain anatomy; ABC fit; treatment via parameter perturbation.基于小鼠脑解剖的患者特异性GBM异种移植生长/侵袭模型；ABC拟合；通过扰动参数模拟治疗。
Drug and single-cell gene expression integration identifies sensitive and resistant glioblastoma cell populations
Suter et al. · Nature Communications · 2026-01-07
scFOCAL/ISOSCELES links GBM cell states to L1000 signatures; predicts state-specific sensitivity and OLIG2i+Depatux-M combo.scFOCAL/ISOSCELES：把GBM细胞状态接到L1000药签，预测状态敏感度并验证OLIG2抑制剂+Depatux-M。
Cited in:引用于： Mesenchymal-like (MES-like) glioblastoma cell state胶质母细胞瘤间充质样（MES 样）细胞状态 · Neural-progenitor-like (NPC-like) and OPC-like glioma cell states神经前体样（NPC 样）与少突前体样（OPC 样）胶质瘤细胞状态 · AI virtual cell modelsAI 虚拟细胞模型 · Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR胶质母细胞瘤扰动数据：放疗、药物与 CRISPR · Therapy-induced plasticity in glioblastoma胶质母细胞瘤的治疗诱导可塑性
Training transformers to solve cancer trial failure — Ron Alfa & Daniel Bear, Noetik
Latent Space · Latent Space podcast · 2026
Noetik's tumor spatial 'virtual cell' models (TARIO-2) and $50M GSK deal.Noetik肿瘤空间虚拟细胞模型TARIO-2与GSK合作。
RECOMB 2026
RECOMB · recomb.org · 2026
RECOMB 2026 (RECOMB 2027 reportedly Toronto; dates not confirmed).RECOMB 2026；2027据称在多伦多（日期未确认）。
scPRINT-2: Towards the next-generation of cell foundation models and benchmarks
Kalfon J, Peyré G, Cantini L. · bioRxiv · 2025-12-15
scPRINT-2: next-gen cell foundation models and benchmarks.scPRINT-2：新一代细胞基础模型与基准。
A single-cell atlas of RNA alternative splicing in the glioma-immune ecosystem.
Song X, Tiek D, Lu M, Yu X, Wu R, Walker M, He Q, Sisbarro D, Hu B, Cheng SY. · Genome biology · 2025-12-12
Single-cell atlas of alternative splicing in glioma-immune ecosystem.胶质瘤-免疫生态可变剪接单细胞图谱。
AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.
Ma C, Zhang H, Rao Y, Jiang X, Liu B, Sun Z, Song Z, Gao Y, Cui Y, Liu X, Li Z. · NPJ digital medicine · 2025-12-11
Review of AI virtual cell models in preclinical research: technical pathways and validation.综述：AI虚拟细胞在临床前研究中的技术路径与验证机制。
Novae: a graph-based foundation model for spatial transcriptomics data.
Blampey Q, Benkirane H, Bercovici N, Mulder K, Gessain G, Ginhoux F, André F, Cournède PH. · Nature methods · 2025-12-10
Novae: graph-based foundation model for spatial transcriptomics.Novae：基于图的空间转录组基础模型。
NeurIPS 2025: Altos Labs wins Generalist Prize at Arc's Virtual Cell Challenge
GEN (Fay Lin) · GEN · 2025-12-08
GEN coverage of challenge results, quotes Hani Goodarzi.GEN报道挑战赛结果。
Virtual Cell Challenge 2025 Wrap-Up: Winners and Reflections
Arc Institute · Arc news · 2025-12
Winners: BioMap (1st), Sichuan Univ XLearning (2nd), Outlier (3rd), Altos Labs (Generalist); metrics gaming lessons.2025挑战赛结果：百图生科第一、四川大学第二；指标可被“刷分”的教训。
A foundation model for brain tumor MRI analysis: WHO grading and subtype classification.
Li J, Liu R, Xing Y, Gao X, Yin Q, Su Q. · Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2025-11-19
Foundation model for brain tumor MRI: WHO grading & subtyping.脑肿瘤MRI基础模型：WHO分级与亚型。
STPath: a generative foundation model for integrating spatial transcriptomics and whole-slide images.
Huang T, Liu T, Babadi M, Ying R, Jin W. · NPJ digital medicine · 2025-11-14
STPath: generative model linking WSIs and spatial transcriptomics.STPath：整合全切片图像与空间转录组的生成模型。
BRD2 bromodomain-mediated regulation of cell state plasticity modulates therapy response in glioblastoma.
Vadla R et al. · Neuro-oncology · 2025-11-01
BRD2 regulates GBM cell-state plasticity and therapy response.BRD2调控GBM状态可塑性与治疗反应。
Cited in:引用于： Mesenchymal-like (MES-like) glioblastoma cell state胶质母细胞瘤间充质样（MES 样）细胞状态 · Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR胶质母细胞瘤扰动数据：放疗、药物与 CRISPR · Therapy-induced plasticity in glioblastoma胶质母细胞瘤的治疗诱导可塑性
Nicheformer: a foundation model for single-cell and spatial omics.
Tejada-Lapuerta A, Schaar AC, Gutgesell R, Palla G, Halle L, Minaeva M, Vornholz L, Dony L, Drummer F, Richter T, Bahrami M, Theis FJ. · Nature methods · 2025-10-30
Nicheformer (Theis lab): single-cell + spatial foundation model, Nat Methods 2025.Nicheformer：单细胞+空间组学基础模型（Theis团队）。
Charting the single-cell and spatial landscape of IDH-wild-type glioblastoma with GBmap.
Ruiz-Moreno C et al. · Neuro-oncology · 2025-10-01
GBmap: single-cell & spatial reference atlas of IDH-wt GBM (>1M cells).GBmap：IDH野生型GBM单细胞与空间参考图谱。
Cited in:引用于： Mesenchymal-like (MES-like) glioblastoma cell state胶质母细胞瘤间充质样（MES 样）细胞状态 · Neural-progenitor-like (NPC-like) and OPC-like glioma cell states神经前体样（NPC 样）与少突前体样（OPC 样）胶质瘤细胞状态 · Glioblastoma single-cell and spatial datasets胶质母细胞瘤单细胞与空间数据集 · Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)Neftel 式 GBM 状态判定能否跨平台复现？GlioState 再分析（初稿）
DELPHAI, AI Agent for Predicting Drug Response and Resistance
Peng et al. · preprint · 2025-09-17
DELPHAI: AI agent for sc perturbation prediction + LLM mechanism on GBM organoids with TMZ; OT+PCA beat baselines.DELPHAI：GBM类器官+TMZ的单细胞扰动预测与LLM机制解读；最优传输+PCA优于基线。
Cited in:引用于： AI virtual cell modelsAI 虚拟细胞模型 · Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR胶质母细胞瘤扰动数据：放疗、药物与 CRISPR · Therapy-induced plasticity in glioblastoma胶质母细胞瘤的治疗诱导可塑性
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 · 2025-08-25
Systema: evaluating perturbation prediction beyond systematic variation.Systema：剔除系统性变异后评估扰动预测。
Cited in:引用于： AI virtual cell modelsAI 虚拟细胞模型 · Validating virtual cells: benchmarks, baselines and critiques虚拟细胞的验证：基准、基线与批评
rBio: reasoning model trained on virtual cell simulations
Chan Zuckerberg Initiative · CZI blog · 2025-08-20
rBio: Qwen2.5-3B reasoning LLM post-trained with TranscriptFormer as soft verifier.rBio：以TranscriptFormer为软验证器训练的生物推理大模型。
Multiscale mathematical model-informed reinforcement learning optimizes combination treatment scheduling in glioblastoma evolution.
Liu Z, Zhang J, Hong L, Nie Q, Sun X. · Science advances · 2025-08-08
Multiscale mechanistic model + reinforcement learning to optimize glioma combination scheduling.多尺度机制模型+强化学习优化胶质瘤联合治疗时序。
Cited in:引用于： Therapy-induced plasticity in glioblastoma胶质母细胞瘤的治疗诱导可塑性
Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines.
Ahlmann-Eltze C, Huber W, Anders S. · Nature methods · 2025-08-04
Ahlmann-Eltze, Huber, Anders: deep models don't beat simple linear baselines for perturbation prediction.Ahlmann-Eltze等：深度模型未超越简单线性基线。
Cited in:引用于： Mesenchymal-like (MES-like) glioblastoma cell state胶质母细胞瘤间充质样（MES 样）细胞状态 · AI virtual cell modelsAI 虚拟细胞模型 · Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR胶质母细胞瘤扰动数据：放疗、药物与 CRISPR · Therapy-induced plasticity in glioblastoma胶质母细胞瘤的治疗诱导可塑性 · Validating virtual cells: benchmarks, baselines and critiques虚拟细胞的验证：基准、基线与批评 · Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)Neftel 式 GBM 状态判定能否跨平台复现？GlioState 再分析（初稿）
Human interpretable grammar encodes multicellular systems biology models to democratize virtual cell laboratories.
Johnson JAI et al. · Cell · 2025-07-26
Human-interpretable grammar for multicellular agent-based models (PhysiCell) to democratize virtual cell labs.可解释“语法”驱动的多细胞主体模型，降低虚拟细胞实验室门槛（含肿瘤示例）。
Can AI build a virtual cell? Scientists race to model life's smallest unit.
Callaway E. · Nature · 2025-07-01
Nature news feature: the race to build an AI virtual cell.Nature新闻特稿：科学家竞相构建AI虚拟细胞。
Single-cell analysis reveals a longitudinal trajectory of meningioma evolution and heterogeneity.
Lee JY, Lee EJ, Seo BY, Kim J, Song Y, Lee D, Moon N, Koo H, Park CK, Kim MS, Park SI, Nam DH, Kong DS, Sa JK. · Nature communications · 2025-07-01
Longitudinal single-cell trajectory of meningioma evolution.脑膜瘤纵向单细胞演化轨迹。
Multimodal Benchmarking of Foundation Model Representations for Cellular Perturbation Response Prediction
Hasanaj E, Cole E, Mohammadi S, Addagudi S, Zhang X, Song L, Xing EP. · bioRxiv · 2025-06-28
Multimodal benchmarking of FM representations for perturbation response.多模态基础模型表示的扰动预测基准。
Predicting cellular responses to perturbation across diverse contexts with State
Adduri AK et al. · bioRxiv · 2025-06-27
State preprint (bioRxiv June 2025).State模型预印本。
X-Atlas/Orion: Genome-wide Perturb-seq Datasets via a Scalable Fix-Cryopreserve Platform for Training Dose-Dependent Biological Foundation Models
Huang AC et al. · bioRxiv · 2025-06-16
X-Atlas/Orion: genome-wide Perturb-seq datasets (Xaira).X-Atlas/Orion：Xaira全基因组Perturb-seq数据集。
Virtual Cell Challenge: Toward a Turing test for the virtual cell.
Roohani YH et al. · Cell · 2025-06-01
Arc Institute perspective launching the Virtual Cell Challenge as a 'Turing test' for virtual cells.Arc研究所发起虚拟细胞挑战赛，作为虚拟细胞的“图灵测试”。
Cited in:引用于： AI virtual cell modelsAI 虚拟细胞模型 · Validating virtual cells: benchmarks, baselines and critiques虚拟细胞的验证：基准、基线与批评
Arc Institute's first virtual cell model: State
Arc Institute · Arc news · 2025-06
Announcement of State (~170M observational + 100M perturbed cells).Arc发布State模型新闻。
A visual-omics foundation model to bridge histopathology with spatial transcriptomics.
Chen W et al. · Nature methods · 2025-05-29
OmiCLIP/Loki: visual-omics foundation model bridging H&E and spatial transcriptomics.病理图像与空间转录组桥接的视觉-组学基础模型。
Interactions between cancer cells and immune cells drive transitions to mesenchymal-like states in glioblastoma.
Hara T et al. · Cancer cell · 2025-05-27
Hara et al.: immune–cancer interactions drive MES-like transitions in GBM.免疫细胞驱动GBM向间充质样状态转变。
Cited in:引用于： Mesenchymal-like (MES-like) glioblastoma cell state胶质母细胞瘤间充质样（MES 样）细胞状态 · Therapy-induced plasticity in glioblastoma胶质母细胞瘤的治疗诱导可塑性
Spatial epigenomic niches underlie glioblastoma cell state plasticity
Kint S et al. · bioRxiv · 2025-05-14
Spatial epigenomic niches underlie GBM cell-state plasticity.空间表观生态位决定GBM状态可塑性。
Cited in:引用于： Glioblastoma single-cell and spatial datasets胶质母细胞瘤单细胞与空间数据集 · Therapy-induced plasticity in glioblastoma胶质母细胞瘤的治疗诱导可塑性
Virtual Cells: Predict, Explain, Discover (arXiv 2505.14613)
Recursion / Valence Labs · arXiv · 2025-05
Recursion/Valence perspective: virtual cells should predict, explain, discover via lab-in-the-loop.Recursion观点：虚拟细胞应能预测、解释、发现。
A Cross-Species Generative Cell Atlas Across 1.5 Billion Years of Evolution: The TranscriptFormer Single-cell Model
Pearce JD, Simmonds SE, Mahmoudabadi G, Krishnan L, Palla G, Istrate A, Tarashansky A, Nelson B, Valenzuela O, Li D, Quake SR, Karaletsos T. · bioRxiv · 2025-04-29
TranscriptFormer (CZI): generative cross-species cell atlas, 112M cells, 12 species.CZI的TranscriptFormer：跨12物种、1.12亿细胞的生成式模型。
TranscriptFormer model overview (blog)
Chan Zuckerberg Initiative · CZI blog · 2025-04-29
CZI blog post introducing TranscriptFormer (Theofanis Karaletsos et al.).CZI介绍TranscriptFormer的博客。
Benchmarking foundation cell models for post-perturbation RNA-seq prediction.
Csendes G, Sanz G, Szalay KZ, Szalai B. · BMC genomics · 2025-04-23
Csendes et al.: foundation cell models vs simple baselines for post-perturbation prediction.Csendes等：基础模型扰动预测不如简单基线。
Cited in:引用于： Mesenchymal-like (MES-like) glioblastoma cell state胶质母细胞瘤间充质样（MES 样）细胞状态 · AI virtual cell modelsAI 虚拟细胞模型 · Validating virtual cells: benchmarks, baselines and critiques虚拟细胞的验证：基准、基线与批评 · Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)Neftel 式 GBM 状态判定能否跨平台复现？GlioState 再分析（初稿）
Zero-shot evaluation reveals limitations of single-cell foundation models.
Kedzierska KZ, Crawford L, Amini AP, Lu AX. · Genome biology · 2025-04-18
Kedzierska et al.: zero-shot limitations of scGPT/Geneformer.Kedzierska等：零样本评估揭示单细胞基础模型局限。
Cited in:引用于： Neural-progenitor-like (NPC-like) and OPC-like glioma cell states神经前体样（NPC 样）与少突前体样（OPC 样）胶质瘤细胞状态 · AI virtual cell modelsAI 虚拟细胞模型 · Validating virtual cells: benchmarks, baselines and critiques虚拟细胞的验证：基准、基线与批评 · Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)Neftel 式 GBM 状态判定能否跨平台复现？GlioState 再分析（初稿）
Scaling Large Language Models for Next-Generation Single-Cell Analysis
Rizvi SA et al. · bioRxiv · 2025-04-17
C2S-Scale (Yale/Google): scaling LLMs to 27B for single-cell analysis and virtual screening.C2S-Scale（耶鲁/谷歌）：270亿参数单细胞LLM，含虚拟药物筛选。
Mapping the spatial architecture of glioblastoma from core to edge delineates niche-specific tumor cell states and intercellular interactions
Khan SM et al. · bioRxiv · 2025-04-04
Spatial architecture of GBM from core to edge; niche-specific states.GBM核心到边缘的空间结构。
Cited in:引用于： Mesenchymal-like (MES-like) glioblastoma cell state胶质母细胞瘤间充质样（MES 样）细胞状态 · Glioblastoma single-cell and spatial datasets胶质母细胞瘤单细胞与空间数据集 · Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)Neftel 式 GBM 状态判定能否跨平台复现？GlioState 再分析（初稿）
Single-cell and spatial genomic landscape of non-small cell lung cancer brain metastases.
Tagore S et al. · Nature medicine · 2025-02-27
Single-cell & spatial landscape of NSCLC brain metastases (Nat Med 2025).非小细胞肺癌脑转移单细胞与空间图谱。
Tahoe-100M : A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling
Zhang J et al. · bioRxiv · 2025-02-24
Tahoe-100M preprint: 100M cells, 1,100 drugs x 50 cancer cell lines.Tahoe-100M：1亿细胞、1100药物×50肿瘤细胞系。
Cited in:引用于： Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR胶质母细胞瘤扰动数据：放疗、药物与 CRISPR
A perturbation proteomics-based foundation model for virtual cell construction
Sun R, Qian L, Li Y, Cheng H, Xue Z, Zhang X, Tan L, Zhan Y, Hu W, Xiao Q, Liu Z, Zhang G, Weinan E, Zhou P, Wen H, Zhu Y, Guo T. · bioRxiv · 2025-02-10
Preprint of the perturbation-proteomics foundation model for virtual cells.上述扰动蛋白组虚拟细胞模型预印本。
scGPT-spatial: Continual Pretraining of Single-Cell Foundation Model for Spatial Transcriptomics
Wang C, Cui H, Zhang A, Xie R, Goodarzi H, Wang B. · bioRxiv · 2025-02-08
scGPT-spatial: continual pretraining for spatial transcriptomics.scGPT-spatial：面向空间转录组的持续预训练。
Build the virtual cell with artificial intelligence: a perspective for cancer research.
Yang T, Wang YY, Ma F, Xu BH, Qian HL. · Military Medical Research · 2025-01-27
Commentary on building virtual cells for cancer research (Chinese authors).面向肿瘤研究的虚拟细胞评论（中国团队）。
CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data.
CZI Cell Science Program et al. · Nucleic acids research · 2025-01-01
CZ CELLxGENE Discover: 100M+ cell data platform (contains glioma datasets).CZ CELLxGENE：超1亿细胞的数据平台（含胶质瘤数据集）。
Cited in:引用于： Glioblastoma single-cell and spatial datasets胶质母细胞瘤单细胞与空间数据集
Scaling Dense Representations for Single Cell with Transcriptome-Scale Context
Ho N, Ellington CN, Hou J, Addagudi S, Mo S, Tao T, Li D, Zhuang Y, Wang H, Cheng X, Song L, Xing EP. · bioRxiv · 2024-12-03
AIDO.Cell (GenBio AI): dense transcriptome-scale context single-cell models.GenBio AI的AIDO.Cell：全转录组上下文的稠密单细胞模型。
How to build the virtual cell with artificial intelligence: Priorities and opportunities.
Bunne C et al. · Cell · 2024-12-01
Landmark perspective defining the AI Virtual Cell (AIVC): multiscale, multimodal, universal representations and virtual instruments.奠基性综述：提出“AI虚拟细胞”定义与多尺度、多模态路线图。
Cited in:引用于： Mesenchymal-like (MES-like) glioblastoma cell state胶质母细胞瘤间充质样（MES 样）细胞状态 · AI virtual cell modelsAI 虚拟细胞模型
A method for in silico exploration of potential glioblastoma multiforme attractors using single-cell RNA sequencing.
Vieira Junior MG, de Almeida Côrtes AM, Gonçalves Carneiro FR, Carels N, Silva FABD. · Scientific reports · 2024-10-29
In silico exploration of GBM attractors from scRNA-seq (Boolean network).基于单细胞数据的GBM吸引子计算探索。
Cited in:引用于： Mesenchymal-like (MES-like) glioblastoma cell state胶质母细胞瘤间充质样（MES 样）细胞状态
GeneCompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model.
Yang X et al. · Cell research · 2024-10-08
GeneCompass: knowledge-informed cross-species foundation model (CAS).GeneCompass：知识引导的跨物种基础模型（中科院）。
In vivo perturb-seq of cancer and microenvironment cells dissects oncologic drivers and radiotherapy responses in glioblastoma.
Liu SJ et al. · Genome biology · 2024-10-07
In vivo Perturb-seq of GBM and microenvironment dissects drivers and radiotherapy responses.GBM体内Perturb-seq：解析驱动基因与放疗反应——脑肿瘤虚拟细胞的关键训练数据类型。
Cited in:引用于： Mesenchymal-like (MES-like) glioblastoma cell state胶质母细胞瘤间充质样（MES 样）细胞状态 · Glioblastoma single-cell and spatial datasets胶质母细胞瘤单细胞与空间数据集 · Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR胶质母细胞瘤扰动数据：放疗、药物与 CRISPR · Therapy-induced plasticity in glioblastoma胶质母细胞瘤的治疗诱导可塑性 · Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)Neftel 式 GBM 状态判定能否跨平台复现？GlioState 再分析（初稿）
PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction
Wenteler A, Occhetta M, Branson N, Curean V, Huebner M, Dee W, Connell W, Chung SP, Hawkins-Hooker A, Ektefaie Y, Valdez Córdova CM, Gallagher-Syed A. · bioRxiv · 2024-10-03
PertEval-scFM: benchmarking scFMs for perturbation effect prediction.PertEval-scFM：扰动预测基准。
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 · 2024-06-06
scFoundation: 100M-parameter model on ~50M human cells (Tsinghua/BioMap).scFoundation：清华/百图生科的1亿参数单细胞基础模型。
Cited in:引用于： AI virtual cell modelsAI 虚拟细胞模型
Hypoxia coordinates the spatial landscape of myeloid cells within glioblastoma to affect survival.
Haley MJ et al. · Science advances · 2024-05-17
Hypoxia coordinates spatial landscape of myeloid cells in GBM.缺氧协调GBM髓系细胞空间格局。
Cited in:引用于： Mesenchymal-like (MES-like) glioblastoma cell state胶质母细胞瘤间充质样（MES 样）细胞状态 · Glioblastoma single-cell and spatial datasets胶质母细胞瘤单细胞与空间数据集
Integrative spatial analysis reveals a multi-layered organization of glioblastoma.
Greenwald AC et al. · Cell · 2024-04-22
Greenwald et al.: multi-layered spatial organization of GBM.Greenwald等：GBM多层次空间组织。
Cited in:引用于： Mesenchymal-like (MES-like) glioblastoma cell state胶质母细胞瘤间充质样（MES 样）细胞状态 · Glioblastoma single-cell and spatial datasets胶质母细胞瘤单细胞与空间数据集
Towards a general-purpose foundation model for computational pathology.
Chen RJ et al. · Nature medicine · 2024-03-19
UNI: general-purpose computational pathology foundation model (incl. brain tumor tasks).UNI：通用病理基础模型（含脑肿瘤分型任务）。
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 · 2024-02-26
scGPT: generative pretrained transformer on 33M cells (Bo Wang lab).scGPT：基于3300万细胞的生成式单细胞基础模型（王波团队）。
Cited in:引用于： Neural-progenitor-like (NPC-like) and OPC-like glioma cell states神经前体样（NPC 样）与少突前体样（OPC 样）胶质瘤细胞状态 · AI virtual cell modelsAI 虚拟细胞模型
A simple agent-based hybrid model to simulate the biophysics of glioblastoma multiforme cells and the concomitant evolution of the oxygen field.
Saucedo-Mora L, Sanz MÁ, Montáns FJ, Benítez JM. · Computer methods and programs in biomedicine · 2024-01-26
Agent-based hybrid model of GBM cell biophysics and treatment.GBM细胞生物物理主体模型。
Disentanglement of single-cell data with biolord.
Piran Z, Cohen N, Hoshen Y, Nitzan M. · Nature biotechnology · 2024-01-15
biolord: disentangled representations for counterfactual cell prediction.biolord：解耦表示进行反事实细胞预测。
Universal Cell Embeddings: A Foundation Model for Cell Biology
Rosen Y, Roohani Y, Agrawal A, Samotorcan L, Tabula Sapiens Consortium, Quake SR, Quake SR, Leskovec J. · bioRxiv · 2023-11-29
Universal Cell Embeddings (UCE), cross-species zero-shot cell embedding (Leskovec/Quake).UCE：跨物种零样本细胞嵌入模型（Leskovec/Quake）。
CellPLM: Pre-training of Cell Language Model Beyond Single Cells
Wen H, Tang W, Dai X, Ding J, Jin W, Xie Y, Tang J. · bioRxiv · 2023-10-05
CellPLM: cell language model beyond single cells (spatial context).CellPLM：引入空间关系的细胞语言模型。
Learning single-cell perturbation responses using neural optimal transport.
Bunne C, Stark SG, Gut G, Del Castillo JS, Levesque M, Lehmann KV, Pelkmans L, Krause A, Rätsch G. · Nature methods · 2023-09-28
CellOT: neural optimal transport for single-cell perturbation responses.CellOT：神经最优传输预测扰动反应。
Cell2Sentence: Teaching Large Language Models the Language of Biology
Levine D et al. · bioRxiv · 2023-09-14
Cell2Sentence: cells as text for LLMs.Cell2Sentence：把细胞转为“句子”供大语言模型学习。
Predicting transcriptional outcomes of novel multigene perturbations with GEARS.
Roohani Y, Huang K, Leskovec J. · Nature biotechnology · 2023-08-17
GEARS: knowledge-graph GNN predicting multigene perturbation outcomes.GEARS：知识图谱图神经网络预测多基因扰动。
Cited in:引用于： AI virtual cell modelsAI 虚拟细胞模型
Single-cell transcriptomic analyses provide insights into the cellular origins and drivers of brain metastasis from lung adenocarcinoma.
Wang Z, Wang Y, Chang M, Wang Y, Liu P, Wu J, Wang G, Tang X, Hui X, Liu P, Guo X, Xing B, Wang Y, Han Z, Ma W. · Neuro-oncology · 2023-07-01
Cellular origins and drivers of brain metastasis by scRNA-seq.脑转移细胞起源与驱动。
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 · 2023-05-31
Geneformer: context-aware transformer enabling in silico deletion/network predictions.Geneformer：支持“计算机模拟敲除”的单细胞基础模型。
Cited in:引用于： Neural-progenitor-like (NPC-like) and OPC-like glioma cell states神经前体样（NPC 样）与少突前体样（OPC 样）胶质瘤细胞状态 · AI virtual cell modelsAI 虚拟细胞模型
Predicting cellular responses to complex perturbations in high-throughput screens.
Lotfollahi M et al. · Molecular systems biology · 2023-05-08
CPA: compositional perturbation autoencoder for drug/dose/combination responses.CPA：组合扰动自编码器，预测药物/剂量/组合反应。
Cited in:引用于： AI virtual cell modelsAI 虚拟细胞模型
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 · 2022-12-20
Single-cell atlas of GBM evolution under therapy.治疗下GBM演化单细胞图谱。
Cited in:引用于： Mesenchymal-like (MES-like) glioblastoma cell state胶质母细胞瘤间充质样（MES 样）细胞状态 · Glioblastoma single-cell and spatial datasets胶质母细胞瘤单细胞与空间数据集 · Therapy-induced plasticity in glioblastoma胶质母细胞瘤的治疗诱导可塑性 · Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)Neftel 式 GBM 状态判定能否跨平台复现？GlioState 再分析（初稿）
The landscape of tumor cell states and spatial organization in H3-K27M mutant diffuse midline glioma across age and location.
Liu I et al. · Nature genetics · 2022-12-05
Liu et al.: tumor cell states and spatial organization of H3-K27M DMG across ages.H3-K27M弥漫中线胶质瘤跨年龄细胞状态与空间组织。
Cited in:引用于： Neural-progenitor-like (NPC-like) and OPC-like glioma cell states神经前体样（NPC 样）与少突前体样（OPC 样）胶质瘤细胞状态
The spatial transcriptomic landscape of non-small cell lung cancer brain metastasis.
Zhang Q, Abdo R, Iosef C, Kaneko T, Cecchini M, Han VK, Li SS. · Nature communications · 2022-10-10
Spatial transcriptomic landscape of NSCLC brain metastasis.肺癌脑转移空间转录组。
Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq.
Replogle JM et al. · Cell · 2022-06-09
Genome-scale Perturb-seq (Replogle et al.): K562/RPE1 benchmark data.全基因组Perturb-seq（Replogle）：扰动预测标准数据。
Cited in:引用于： AI virtual cell modelsAI 虚拟细胞模型 · Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR胶质母细胞瘤扰动数据：放疗、药物与 CRISPR
Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma.
Ravi VM et al. · Cancer cell · 2022-06-01
Ravi et al.: spatial multi-omics of tumor-host interdependence in GBM.Ravi等：GBM空间多组学揭示肿瘤-宿主相互依存。
Cited in:引用于： Glioblastoma single-cell and spatial datasets胶质母细胞瘤单细胞与空间数据集
Single-cell profiling of human dura and meningioma reveals cellular meningeal landscape and insights into meningioma immune response.
Wang AZ et al. · Genome medicine · 2022-05-10
Single-cell profiling of human dura and meningioma.硬脑膜与脑膜瘤单细胞图谱。
scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data
Yang F, Wang W, Wang F, Fang Y, Tang D, Huang J, Lu H, Yao J. · bioRxiv · 2021-12-07
scBERT: early pretrained language model for cell type annotation.scBERT：早期细胞注释预训练模型。
Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy.
Couturier CP et al. · Nature communications · 2020-07-08
Couturier et al.: GBM recapitulates neurodevelopmental hierarchy.GBM重演神经发育层级。
Cited in:引用于： Neural-progenitor-like (NPC-like) and OPC-like glioma cell states神经前体样（NPC 样）与少突前体样（OPC 样）胶质瘤细胞状态
A Patient-Derived Glioblastoma Organoid Model and Biobank Recapitulates Inter- and Intra-tumoral Heterogeneity.
Jacob F et al. · Cell · 2019-12-26
Jacob et al.: patient-derived GBM organoid biobank (GBO).Jacob等：患者来源GBM类器官库。
Cited in:引用于： Neural-progenitor-like (NPC-like) and OPC-like glioma cell states神经前体样（NPC 样）与少突前体样（OPC 样）胶质瘤细胞状态 · Glioblastoma single-cell and spatial datasets胶质母细胞瘤单细胞与空间数据集
Single-Cell Transcriptomics in Medulloblastoma Reveals Tumor-Initiating Progenitors and Oncogenic Cascades during Tumorigenesis and Relapse.
Zhang L et al. · Cancer cell · 2019-08-29
Vladoiu et al.: medulloblastoma progenitors & developmental origins.髓母细胞瘤起源祖细胞。
scGen predicts single-cell perturbation responses.
Lotfollahi M, Wolf FA, Theis FJ. · Nature methods · 2019-07-29
scGen: early VAE-based single-cell perturbation response prediction.scGen：早期基于VAE的单细胞扰动预测。
Resolving medulloblastoma cellular architecture by single-cell genomics.
Hovestadt V et al. · Nature · 2019-07-24
Hovestadt et al.: medulloblastoma cellular architecture by single-cell genomics.髓母细胞瘤单细胞结构。
An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma.
Neftel C et al. · Cell · 2019-07-18
Neftel et al.: four GBM malignant states (NPC/OPC/AC/MES), plasticity and genetics.Neftel等：GBM四种恶性细胞状态及可塑性——虚拟细胞建模的核心“状态空间”。
Cited in:引用于： Mesenchymal-like (MES-like) glioblastoma cell state胶质母细胞瘤间充质样（MES 样）细胞状态 · Neural-progenitor-like (NPC-like) and OPC-like glioma cell states神经前体样（NPC 样）与少突前体样（OPC 样）胶质瘤细胞状态 · Glioblastoma single-cell and spatial datasets胶质母细胞瘤单细胞与空间数据集 · Therapy-induced plasticity in glioblastoma胶质母细胞瘤的治疗诱导可塑性 · Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)Neftel 式 GBM 状态判定能否跨平台复现？GlioState 再分析（初稿）
Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.
Filbin MG et al. · Science (New York, N.Y.) · 2018-04-01
Filbin et al.: OPC-like programs in H3K27M gliomas.Filbin等：H3K27M胶质瘤的发育与致瘤程序。
Cited in:引用于： Neural-progenitor-like (NPC-like) and OPC-like glioma cell states神经前体样（NPC 样）与少突前体样（OPC 样）胶质瘤细胞状态
Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq.
Venteicher AS et al. · Science (New York, N.Y.) · 2017-03-01
Venteicher et al.: IDH-mutant glioma lineages and microenvironment by scRNA-seq.Venteicher等：IDH突变胶质瘤单细胞谱系。
Cited in:引用于： Neural-progenitor-like (NPC-like) and OPC-like glioma cell states神经前体样（NPC 样）与少突前体样（OPC 样）胶质瘤细胞状态
Glioma follow white matter tracts: a multiscale DTI-based model.
Engwer C, Hillen T, Knappitsch M, Surulescu C. · Journal of mathematical biology · 2014-09-12
Multiscale DTI-based glioma spread model.基于DTI的多尺度胶质瘤扩散模型。
Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion.
Swanson KR, Bridge C, Murray JD, Alvord EC. · Journal of the neurological sciences · 2003-12-01
Swanson et al.: 'virtual and real brain tumors' — classic proliferation-invasion model.Swanson等：“虚拟脑肿瘤”经典增殖-侵袭模型。
CZI Virtual Cells Platform
Chan Zuckerberg Initiative · web · date n/a日期未注明
CZI hub hosting virtual cell models (TranscriptFormer, rBio, etc.).CZI虚拟细胞模型平台。
Arc Institute Virtual Cell Initiative
Arc Institute · web · date n/a日期未注明
Arc's program combining Perturb-seq data generation, State, and the Challenge.Arc虚拟细胞计划。
AIDO Cell: a general-purpose simulator for cell biology
GenBio AI · GenBio web · date n/a日期未注明
Stateful multimodal virtual cell simulator (K562, HepG2).GenBio的有状态多模态细胞模拟器。
Bioptimus: multimodal biology foundation model (NVIDIA compute)
Bioptimus · Bioptimus news · date n/a日期未注明
French startup building multiscale biology FMs (H-Optimus pathology models).法国Bioptimus多尺度生物基础模型。
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