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).
Library
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AACR Annual Meeting 2027
AACR · Orlando, FL (to Apr 7) · 2027-04-02
AACR 2027, Apr 2-7, Orlando.
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.
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).
AgenticLS @ NeurIPS 2026 — Agentic AI for Biological Discovery
NeurIPS · NeurIPS 2026, Sydney · 2026-12-11
NeurIPS 2026 workshop on agentic AI for biology.
SIMBIOCHEM II · NeurIPS 2026 Workshop on ML for Simulation
NeurIPS · NeurIPS 2026, Sydney · 2026-12-11
NeurIPS 2026 workshop on ML for biochemical simulation.
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.
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.
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.
Cited in: Neural-progenitor-like (NPC-like) and OPC-like glioma cell states
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.
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).
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.
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).
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.
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).
Cited in: Perturbation data for glioblastoma: radiotherapy, drugs and 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).
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.
Cited in: AI virtual cell models
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.
Cited in: AI virtual cell models · Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR · Validating virtual cells: benchmarks, baselines and critiques
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.
Cited in: AI virtual cell models · Validating virtual cells: benchmarks, baselines and critiques
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).
AIDO Cell: a general-purpose simulator for cell biology
GenBio AI · GenBio web · 2026-08-18
Stateful multimodal virtual cell simulator (K562, HepG2).
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).
Cited in: Validating virtual cells: benchmarks, baselines and critiques
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.
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.
Cited in: Perturbation data for glioblastoma: radiotherapy, drugs and 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.
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).
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.
Cited in: Validating virtual cells: benchmarks, baselines and critiques
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.
Cited in: Validating virtual cells: benchmarks, baselines and critiques
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.
ISMB 2026
ISCB · Washington, DC (to Jul 16) · 2026-07-12
ISMB 2026, Jul 12-16, Washington DC (from search summary).
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.
A World Model of the Virtual Cell (ICML 2026)
ICML 2026 · ICML 2026 · 2026-07
Proposes virtual cell world model (VCWM) as operational definition.
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.
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).
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.
Cited in: Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR · Therapy-induced plasticity in glioblastoma
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.
RECOMB 2026
RECOMB · Thessaloniki, Greece (to May 29) · 2026-05-26
RECOMB 2026 (RECOMB 2027 reportedly Toronto; dates not confirmed).
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.
Cited in: Validating virtual cells: benchmarks, baselines and critiques
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.
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.
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.
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.
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).
Cited in: AI virtual cell models
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.
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.
Cited in: Validating virtual cells: benchmarks, baselines and critiques
Training transformers to solve cancer trial failure — Ron Alfa & Daniel Bear, Noetik
Latent Space · Latent Space podcast · 2026-04-20
Noetik's tumor spatial 'virtual cell' models (TARIO-2) and $50M GSK deal.
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.
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.
Xaira's first virtual cell model is largest to-date
GEN · GEN · 2026-03-25
GEN on X-Cell; includes Noetik CEO Ron Alfa's tissue-first counterpoint.
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.
Cited in: AI virtual cell models · Validating virtual cells: benchmarks, baselines and critiques
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.
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.
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.
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.
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.
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.
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.
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.
Cited in: Mesenchymal-like (MES-like) glioblastoma cell state · Neural-progenitor-like (NPC-like) and OPC-like glioma cell states · AI virtual cell models · Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR · Therapy-induced plasticity in glioblastoma
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.
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.
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.
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.
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.
Virtual Cell Challenge 2025 Wrap-Up: Winners and Reflections
Arc Institute · Arc news · 2025-12-06
Winners: BioMap (1st), Sichuan Univ XLearning (2nd), Outlier (3rd), Altos Labs (Generalist); metrics gaming lessons.
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.
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.
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.
Cited in: Mesenchymal-like (MES-like) glioblastoma cell state · Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR · Therapy-induced plasticity in glioblastoma
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.
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).
Cited in: Mesenchymal-like (MES-like) glioblastoma cell state · Neural-progenitor-like (NPC-like) and OPC-like glioma cell states · Glioblastoma single-cell and spatial datasets · Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)
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.
Cited in: AI virtual cell models · Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR · Therapy-induced plasticity in glioblastoma
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.
Cited in: AI virtual cell models · Validating virtual cells: benchmarks, baselines and critiques
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.
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.
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.
Cited in: Mesenchymal-like (MES-like) glioblastoma cell state · AI virtual cell models · Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR · Therapy-induced plasticity in glioblastoma · Validating virtual cells: benchmarks, baselines and critiques · Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)
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.
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.
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.
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.
Predicting cellular responses to perturbation across diverse contexts with State
Adduri AK et al. · bioRxiv · 2025-06-27
State preprint (bioRxiv June 2025).
Arc Institute's first virtual cell model: State
Arc Institute · Arc news · 2025-06-23
Announcement of State (~170M observational + 100M perturbed cells).
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).
Bioptimus: multimodal biology foundation model (NVIDIA compute)
Bioptimus · Bioptimus news · 2025-06-11
French startup building multiscale biology FMs (H-Optimus pathology models).
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.
Cited in: AI virtual cell models · Validating virtual cells: benchmarks, baselines and critiques
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.
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.
Cited in: Mesenchymal-like (MES-like) glioblastoma cell state · Therapy-induced plasticity in glioblastoma
Virtual Cells: Predict, Explain, Discover (arXiv 2505.14613)
Recursion / Valence Labs · arXiv · 2025-05-20
Recursion/Valence perspective: virtual cells should predict, explain, discover via lab-in-the-loop.
Spatial epigenomic niches underlie glioblastoma cell state plasticity
Kint S et al. · bioRxiv · 2025-05-14
Spatial epigenomic niches underlie GBM cell-state plasticity.
Cited in: Glioblastoma single-cell and spatial datasets · Therapy-induced plasticity in glioblastoma
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.
TranscriptFormer model overview (blog)
Chan Zuckerberg Initiative · CZI blog · 2025-04-29
CZI blog post introducing TranscriptFormer (Theofanis Karaletsos et al.).
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.
Cited in: Mesenchymal-like (MES-like) glioblastoma cell state · AI virtual cell models · Validating virtual cells: benchmarks, baselines and critiques · Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)
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.
Cited in: Neural-progenitor-like (NPC-like) and OPC-like glioma cell states · AI virtual cell models · Validating virtual cells: benchmarks, baselines and critiques · Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)
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.
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.
Cited in: Mesenchymal-like (MES-like) glioblastoma cell state · Glioblastoma single-cell and spatial datasets · Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)
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).
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.
Cited in: Perturbation data for glioblastoma: radiotherapy, drugs and 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.
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.
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).
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).
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.
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.
Cited in: Mesenchymal-like (MES-like) glioblastoma cell state · AI virtual cell models
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).
Cited in: Mesenchymal-like (MES-like) glioblastoma cell state
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).
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.
Cited in: Mesenchymal-like (MES-like) glioblastoma cell state · Glioblastoma single-cell and spatial datasets · Perturbation data for glioblastoma: radiotherapy, drugs and CRISPR · Therapy-induced plasticity in glioblastoma · Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)
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.
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).
Cited in: AI virtual cell models
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.
Cited in: Mesenchymal-like (MES-like) glioblastoma cell state · Glioblastoma single-cell and spatial datasets
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.
Cited in: Mesenchymal-like (MES-like) glioblastoma cell state · Glioblastoma single-cell and spatial datasets
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).
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).
Cited in: Neural-progenitor-like (NPC-like) and OPC-like glioma cell states · AI virtual cell models
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.
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.
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).
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).
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.
Cell2Sentence: Teaching Large Language Models the Language of Biology
Levine D et al. · bioRxiv · 2023-09-14
Cell2Sentence: cells as text for LLMs.
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.
Cited in: AI virtual cell models
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.
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.
Cited in: Neural-progenitor-like (NPC-like) and OPC-like glioma cell states · AI virtual cell models
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.
Cited in: AI virtual cell models
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.
Cited in: Mesenchymal-like (MES-like) glioblastoma cell state · Glioblastoma single-cell and spatial datasets · Therapy-induced plasticity in glioblastoma · Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)
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.
Cited in: Neural-progenitor-like (NPC-like) and OPC-like glioma cell states
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.
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.
Cited in: AI virtual cell models · Perturbation data for glioblastoma: radiotherapy, drugs and 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.
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.
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.
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.
Cited in: Neural-progenitor-like (NPC-like) and OPC-like glioma cell states
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).
Cited in: Neural-progenitor-like (NPC-like) and OPC-like glioma cell states · Glioblastoma single-cell and spatial datasets
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.
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.
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.
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.
Cited in: Mesenchymal-like (MES-like) glioblastoma cell state · Neural-progenitor-like (NPC-like) and OPC-like glioma cell states · Glioblastoma single-cell and spatial datasets · Therapy-induced plasticity in glioblastoma · Do Neftel-style GBM state calls replicate across platforms? A GlioState re-analysis (draft)
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.
Cited in: Neural-progenitor-like (NPC-like) and OPC-like glioma cell states
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.
Cited in: Neural-progenitor-like (NPC-like) and OPC-like glioma cell states
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.
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.
CZI Virtual Cells Platform
Chan Zuckerberg Initiative · web · date n/a
CZI hub hosting virtual cell models (TranscriptFormer, rBio, etc.).
Arc Institute Virtual Cell Initiative
Arc Institute · web · date n/a
Arc's program combining Perturb-seq data generation, State, and the Challenge.
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