A curated list of papers, datasets, benchmarks, talks, and community resources for AI-powered virtual cell research.
Here, AIVC stands for Artificial Intelligence Virtual Cell, a term popularized by the Cell perspective "How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities." The focus of this repository is broad but practical: resources that help researchers understand, model, benchmark, or build virtual cells and related cellular foundation models.
For scientific figure ideas and plotting templates, see Awesome Scientific Figures.
- Scope
- Inclusion Rules
- Overview Papers
- Research Papers
- Datasets and Benchmarks
- Reports and Blogs
- Videos
- Historical and Foundational Works
- Related Resources
- In scope: virtual cell perspectives, perturbation modeling, single-cell and multimodal foundation models, spatial and morphology modeling, biological AI agents, datasets, benchmarks, and community resources closely connected to virtual cell research.
- Also included: adjacent work that is broadly useful for the virtual cell community, especially when it contributes data, evaluation methods, or modeling tools for cellular systems.
- Usually out of scope: generic biomedical AI work with weak cell-modeling relevance, low-confidence secondary sources, broken links, or items that do not add clear value beyond more central references already listed here.
- Prefer peer-reviewed papers, high-signal preprints, official project pages, and primary-source links.
- Include code, datasets, project pages, or Chinese summaries when they are clearly useful.
- Keep entries concise and broadly reusable for readers who are scanning the field.
- Use lightweight tags in
Research Papersonly as browsing aids. They are intentionally approximate, not rigid taxonomy.
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[Nature News] Can AI Build a Virtual Cell? Scientists Race to Model Life's Smallest Unit (Nature 2025) [paper] [中文解读]
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[Nature Perspective] Towards Multimodal Foundation Models in Molecular Cell Biology (Nature 2025) [paper] [中文解读]
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[Nature] The Human Cell Atlas from a cell census to a unified foundation model (Nature 2024) [paper]
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[Nature Review] Interpretation, extrapolation and perturbation of single cells (Nature Reviews Genetics 2026) [paper]
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[Nature Review] Revisiting the blueprint for an interpretable virtual cell (Nature Reviews Genetics 2026) [paper]
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[npj Digital Medicine] AI-driven virtual cell models in preclinical research: t E901 echnical pathways, validation mechanisms, and clinical translation potential (npj Digital Medicine 2025) [paper]
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[Nature Review] Adapting systems biology to address the complexity of human disease in the single-cell era (Nature Reviews Genetics 2025) [paper]
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[Nature Genetics] Causal machine learning for single-cell genomics (Nature Genetics 2025) [paper]
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[Nature Methods] Multimodal foundation transformer models for multiscale genomics (Nature Methods 2025) [paper]
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[Cell Perspective] Empowering Biomedical Discovery with AI Agents (Cell 2024) [paper] [中文解读]
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[Cell Perspective] How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities (Cell 2024) [paper] [中文解读]
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[Cell Review] Toward a Foundation Model of Causal Cell and Tissue Biology with a Perturbation Cell and Tissue Atlas (Cell 2024) [paper] [中文解读]
Tag hints: [Virtual Cell], [Perturbation], [Foundation Model], [Spatial], [Morphology], [Agent], [Benchmark], [Tool], [Related]. Tags are lightweight and non-exhaustive.
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[Lingshu-Cell]
[Virtual Cell]Lingshu-Cell: A generative cellular world model for transcriptome modeling toward virtual cells (arXiv 2026) [paper] -
[Spatial Perturb-seq]
[Spatial]Spatial perturb-seq: single-cell functional genomics within intact tissue architecture (Nature Communications 2026) [paper][code] -
[CONCORD]
[Foundation Model]Revealing a coherent cell-state landscape across single-cell datasets with CONCORD (Nature Biotechnology 2026) [paper][code] -
[AI Scientist]
[Related]Towards end-to-end automation of AI research (Nature 2026) [paper] [code] -
[CRISPRi Map]
[Perturbation]A genome-scale single-cell CRISPRi map of trans gene regulation across human pluripotent stem cell lines (Cell Genomics 2026) [paper] -
[Therapeutic Design]
[Related]Deep-learning-based de novo discovery and design of therapeutics that reverse disease-associated transcriptional phenotypes (Cell 2026) [paper] -
[X-Pert]
[Perturbation]Unified Multimodal Learning Enables Generalized Cellular Response Prediction to Diverse Perturbations (bioRxiv) [paper] [code][ask deepwiki]
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[MVCBench]
[Benchmark]MVCBench: A Multimodal Benchmark for Drug-induced Virtual Cell Phenotypes (bioRxiv) [paper] [code][ask deepwiki]
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[HarmonyCell]
[Perturbation]HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts (bioRxiv) [paper] -
[scDFM]
[Perturbation]scDFM: Distributional Flow Matching Model for Robust Single-Cell Perturbation Prediction (arXiv 2026) [paper] -
[STRAND]
[Perturbation]STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations (arXiv 2026) [paper] -
[PerturbDiff]
[Perturbation]PerturbDiff: Functional Diffusion for Single-Cell Perturbation Modeling (arXiv 2026) [paper] [project] -
[scBIG]
[Perturbation]Beyond Independent Genes: Learning Module-Inductive Representations for Gene Perturbation Prediction (arXiv 2026) [paper] -
[Stack]
[Foundation Model]Stack: In-Context Learning of Single-Cell Biology (bioRxiv) [paper] [code][ask deepwiki]
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[Pertpy]
[Tool]Pertpy: an End-to-end Framework for Perturbation Analysis (Nature Methods) [paper] [code][ask deepwiki]
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[Benchmarking]
[Benchmark]Benchmarking Algorithms for Generalizable Single-Cell Perturbation Response Prediction (Nature Methods) [paper] [code][ask deepwiki]
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[Scouter]
[Perturbation]Scouter predicts transcriptional responses to genetic perturbations with large language model embeddings (Nature Computational Science 2025) [paper] -
[VCWorld]
[Virtual Cell]VCWorld: A Biological World Model for Virtual Cell Simulation (arXiv) [paper] [code][ask deepwiki]
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[Squidiff]
[Perturbation]Squidiff: Predicting Cellular Development and Responses to Perturbations using a Diffusion Model (Nature Methods) [paper] [code][ask deepwiki]
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[Nicheformer]
[Spatial]Nicheformer: A Foundation Model for Single-Cell and Spatial Omics (Nature Methods) [paper] [中文解读] [code][ask deepwiki]
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[ADLF]
[Perturbation]Active Learning Framework Leveraging Transcriptomics Identifies Modulators of Disease Phenotypes (Science) [paper] [code] -
[Tahoe-x1]
[Foundation Model]Tahoe-x1: Scaling Perturbation-Trained Single-Cell Foundation Models to 3 Billion Parameters (bioRxiv 2025) [paper] [code][ask deepwiki] [hugging face files]
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[LPM]
[Perturbation]In Silico Biological Discovery with Large Perturbation Models (Nature Computational Science 2025) [paper] [中文解读] [code][ask deepwiki]
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[CellNavi]
[Perturbation]CellNavi Predicts Genes Directing Cellular Transitions by Learning a Gene Graph-Enhanced Cell State Manifold (Nature Cell Biology 2025) [paper] [中文解读] [code][ask deepwiki]
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[EpiAgent]
[Foundation Model]EpiAgent: Foundation Model for Single-Cell Epigenomics (Nature Methods 2025) [paper] [中文解读] [code][ask deepwiki]
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[CellWhisperer]
[Tool]Multimodal learning enables chat-based exploration of single-cell data (Nature Biotechnology 2025) [paper] [code] -
[CRISPR-GPT]
[Agent]CRISPR-GPT for Agentic Automation of Gene-Editing Experiments (Nature Biomedical Engineering 2025) [paper] [中文解读] [code][ask deepwiki]
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[Cell-o1]
[Agent]Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning (arXiv 2025) [paper] [code][hugging face] [ask deepwiki]
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[Systema]
[Benchmark]Systema: A Framework for Evaluating Genetic Perturbation Response Prediction Beyond Systematic Variation (Nature Biotechnology 2025) [paper] [中文解读] [code][ask deepwiki]
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[RegVelo]
[Perturbation]RegVelo: Gene-Regulatory-Informed Dynamics of Single Cells (bioRxiv) [paper] [code][ask deepwiki]
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[PhenoProfiler]
[Morphology]PhenoProfiler: Advancing Morphology Representations for Image-based Drug Discovery (Nature Communications 2025) [paper] [code][webserver] [ask deepwiki]
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[MorphDiff]
[Morphology]Prediction of Cellular Morphology Changes under Perturbations with a Transcriptome-Guided Diffusion Model (Nature Communications 2025) [paper] [中文解读] [code][ask deepwiki]
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[rBio-1]
[Agent]rBio1-Training Scientific Reasoning LLMs with Biological World Models as Soft Verifiers (bioRxiv 2025) [paper] [中文解读] [code][ask deepwiki]
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[TranscriptFormer]
[Foundation Model]A Cross-Species Generative Cell Atlas across 1.5 Billion Years of Evolution: The Transcriptformer Single-Cell Model (bioRxiv 2025) [paper] [code][ask deepwiki]
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[CellAtria]
[Agent]An Agentic AI Framework for Ingestion and Standardization of Single-Cell RNA-Seq Data Analysis (bioRxiv 2025) [paper] [中文解读] [code][ask deepwiki]
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[Scvi-hub]
[Tool]Scvi-hub: An Actionable Repository for Model-Driven Single-Cell Analysis (Nature Methods 2025) [paper] [中文解读] [code][ask deepwiki]
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[GraphVelo]
[Spatial]GraphVelo Allows for Accurate Inference of Multimodal Velocities and Molecular Mechanisms for Single Cells (Nature Communications 2025) [paper] [中文解读] [code][ask deepwiki]
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[Stereo-Cell]
[Spatial]Stereo-Cell: Spatial Enhanced-Resolution Single-Cell Sequencing with High-Density DNA Nanoball-Patterned Arrays (Science 2025) [paper] [中文解读] [code][ask deepwiki]
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[SToFM]
[Spatial]SToFM: A Multi-scale Foundation Model for Spatial Transcriptomics (ICML 2025 Poster) [paper] [中文解读] [code][ask deepwiki]
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[scGPT-spatial]
[Spatial]scGPT-spatial: Continual Pretraining of Single-Cell Foundation Model for Spatial Transcriptomics (bioRxiv 2025) [paper] [code] -
[SpatialAgent]
[Agent]SpatialAgent: An Autonomous AI Agent for Spatial Biology (bioRxiv 2025) [paper] [中文解读] [code][ask deepwiki]
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[CellFlux]
[Morphology]CellFlux: Simulating Cellular Morphology Changes via Flow Matching (ICML 2025 Poster) [paper] [code][ask deepwiki]
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[MorphoDiff]
[Morphology]MorphoDiff: Cellular Morphology Painting with Diffusion Models (ICLR 2025) [paper] [preprint] [code] -
[CellPB]
[Benchmark]Benchmarking AI Models for in Silico Gene Perturbation of Cells (bioRxiv 2025) [paper] [code][ask deepwiki]
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[CellForge]
[Agent]CellForge: Agentic Design of Virtual Cell Models (arXiv 2025) [paper] [中文解读] [code][ask deepwiki]
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[Brief Communication]
[Benchmark]Deep-Learning-Based Gene Perturbation Effect Prediction Does Not Yet Outperform Simple Linear Baselines (Nature Methods 2025) [paper] [code][ask deepwiki]
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[Brief Communication]
[Benchmark]Limitations of Cell Embedding Metrics Assessed Using Drifting Islands (Nature Biotechnology 2025) [paper] [code][ask deepwiki]
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[GeneAgent]
[Agent]GeneAgent: Self-Verification Language Agent for Gene-Set Analysis Using Domain Databases (Nature Methods 2025) [paper] [code][ask deepwiki]
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[Theory]
[Virtual Cell]Human Interpretable Grammar Encodes Multicellular Systems Biology Models to Democratize Virtual Cell Laboratories (Cell 2025) [paper] [code][ask deepwiki]
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[GREmLN]
[Foundation Model]GREmLN: A Cellular Regulatory Network-Aware Transcriptomics Foundation Model (bioRxiv 2025) [paper] [中文解读] [code][ask deepwiki]
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[CellVoyager]
[Agent]CellVoyager: AI CompBio Agent Generates New Insights by Autonomously Analyzing Biological Data (bioRxiv 2025) [paper] [中文解读] [code][ask deepwiki]
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[CausCell]
[Perturbation]Causal Disentanglement for Single-Cell Representations and Controllable Counterfactual Generation (Nature Communications 2025) [paper] [中文解读] [code][ask deepwiki]
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[CLIP^n]
[Morphology]Transitive Prediction of Small-Molecule Function through Alignment of High-Content Screening Resources (Nature Biotechnology 2025) [paper] [code][ask deepwiki]
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[DrugPT]
[Perturbation]DrugPT: A Flexible Framework for Integrating Gene and Chemical Representations in Perturbation Modeling (bioRxiv 2025) [paper] -
[OmniPert]
[Perturbation]OmniPert: A Deep Learning Foundation Model for Predicting Responses to Genetic and Chemical Perturbations in Single Cancer Cells (bioRxiv 2025) [paper] -
[UNAGI]
[Perturbation]A Deep Generative Model for Deciphering Cellular Dynamics and in Silico Drug Discovery in Complex Diseases (Nature Biomedical Engineering 2025) [paper] [code][ask deepwiki]
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[OmiCLIP]
[Spatial]A Visual-Omics Foundation Model to Bridge Histopathology with Spatial Transcriptomics (Nature Methods 2025) [paper] [code][ask deepwiki]
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[Biomni]
[Agent]Biomni: A General-Purpose Biomedical AI Agent (bioRxiv 2025) [paper] [code][ask deepwiki]
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[OCTO-vc]
[Virtual Cell]OCTO-vc: Virtual Cells in Real Tissue (© by Noetik 2025) [technical report] [online demonstration] -
[STATE]
[Perturbation]Predicting Cellular Responses to Perturbation across Diverse Contexts with STATE (bioRxiv 2025) [paper] [code][ask deepwiki]
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[UniPert-G2CP]
[Perturbation]Genetic-To-Chemical Perturbation Transfer Learning through Unified Multimodal Molecular Representations (bioRxiv 2025) [paper] [code][ask deepwiki]
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[Cell-GraphCompass]
[Foundation Model]Cell-GraphCompass: Modeling Single Cells with Graph Structure Foundation Model (National Science Review 2025) [paper] [code][ask deepwiki]
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[scPRINT]
[Foundation Model]scPRINT: Pre-training on 50 Million Cells Allows Robust Gene Network Predictions (Nature Communications 2025) [paper] [code][ask deepwiki]
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[CellFM]
[Foundation Model]CellFM: A Large-Scale Foundation Model Pre-trained on Transcriptomics of 100 Million Human Cells (Nature Communications 2025) [paper] [中文解读] [code][ask deepwiki]
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[C2S-Scale]
[Foundation Model]C2S-Scale: Scaling Large Language Models for Next-Generation Single-Cell Analysis (bioRxiv 2025) [paper] [中文解读] [code][ask deepwiki]
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[scNET]
[Foundation Model]scNET: Learning Context-Specific Gene and Cell Embeddings by Integrating Single-Cell Gene Expression Data with Protein-Protein Interactions (Nature Methods 2025) [paper] [code][ask deepwiki]
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[SubCell]
[Foundation Model]SubCell: Proteome-aware vision foundation models for microscopy capture single-cell biology (bioRxiv 2025) [paper] [code][ask deepwiki]
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[Token-Mol 1.0]
[Related]Token-Mol 1.0: Tokenized Drug Design with Large Language Models (Nature Communications 2025) [paper] [code][ask deepwiki]
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[Comment]
[Virtual Cell]Virtual Cells for Predictive Immunotherapy (Nature Biotechnology Comment 2025) [paper] -
[Recursion]
[Virtual Cell]Virtual Cells: Predict, Explain, Discover (arXiv 2025) [paper] -
[Cell Maps]
[Virtual Cell]Multimodal cell maps as a foundation for structural and functional genomics (Nature 2025) [paper] [project] -
[CellFlow]
[Morphology]CellFlow Enables Generative Single-Cell Phenotype Modeling with Flow Matching (bioRxiv 2025) [paper] [code][ask deepwiki]
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[Cell Shapes]
[Morphology]Cell shapes decode molecular phenotypes in image-based spatial proteomics (bioRxiv 2025) [paper] -
[Prophet]
[Perturbation]Scalable and Universal Prediction of Cellular Phenotypes (bioRxiv 2025) [paper] [code][ask deepwiki]
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[Morphology]Evaluating Feature Extraction in Ovarian Cancer Cell Line Co-Cultures Using Deep Neural Networks (Communications Biology 2025) [paper] -
[ProteinTalks]
[Foundation Model]A Perturbation Proteomics-Based Foundation Model for Virtual Cell Construction (bioRxiv 2025) [paper] [中文解读] [code][ask deepwiki]
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[Virtual Cell]Grow AI Virtual Cells: Three Data Pillars and Closed-Loop Learning (Cell Research 2025) [paper] [中文解读] -
[Virtual Cell]Build the Virtual Cell with Artificial Intelligence: A Perspective for Cancer Research (Military Medical Research 2025) [paper] -
[PS]
[Perturbation]Decoding Heterogeneous Single-Cell Perturbation Responses (Nature Cell Biology 2025) [paper] [code][ask deepwiki]
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[Mixscale]
[Perturbation]Systematic Reconstruction of Molecular Pathway Signatures Using Scalable Single-Cell Perturbation Screens (Nature Cell Biology 2025) [paper] [code][ask deepwiki]
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[GET]
[Foundation Model]A Foundation Model of Transcription across Human Cell Types (Nature 2025) [paper] [code][ask deepwiki]
[UniCure] [Perturbation] Unicure: A Foundation Model for Predicting Personalized Cancer Therapy Response (bioRxiv 2025) [paper] [code] 
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[TranSiGen]
[Perturbation]Deep Representation Learning of Chemical-Induced Transcriptional Profile for Phenotype-Based Drug Discovery (Nature Communications 2024) [paper] [code][ask deepwiki]
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[PRnet]
[Perturbation]Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery (Nature Communications 2024) [paper] -
[GenePT]
[Foundation Model]Simple and Effective Embedding Model for Single-Cell Biology Built from ChatGPT (Nature Biomedical Engineering 2024) [paper] [code][ask deepwiki]
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[SCimilarity]
[Foundation Model]A Cell Atlas Foundation Model for Scalable Search of Similar Human Cells (Nature 2024) [paper] [code][ask deepwiki]
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[scLong]
[Foundation Model]scLong: A Billion-Parameter Foundation Model for Capturing Long-Range Gene Context in Single-Cell Transcriptomics (bioRxiv 2024) [paper] [code][ask deepwiki]
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[scFoundation]
[Foundation Model]Large-Scale Foundation Model on Single-Cell Transcriptomics (Nature Methods 2024) [paper] [code][ask deepwiki]
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[scGPT]
[Foundation Model]scGPT: Toward Building a Foundation Model for Single-Cell Multi-Omics Using Generative AI (Nature Methods 2024) [paper] [code][ask deepwiki]
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[TamGen]
[Related]TamGen: Drug Design with Target-Aware Molecule Generation through a Chemical Language Model (Nature Communications 2024) [paper] [code][ask deepwiki]
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[GeneCompass]
[Foundation Model]GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with a Knowledge-Informed Cross-Species Foundation Model (Cell Research 2024) [paper] [code][ask deepwiki]
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[scTab]
[Foundation Model]scTab: Scaling Cross-Tissue Single-Cell Annotation Models (Nature Communications 2024) [paper] [code][ask deepwiki]
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[SATURN]
[Foundation Model]Toward Universal Cell Embeddings: Integrating Single-Cell RNA-Seq Datasets across Species with SATURN (Nature Methods 2024) [paper] [code][ask deepwiki]
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[UCE]
[Foundation Model]Universal Cell Embeddings: A Foundation Model for Cell Biology (bioRxiv 2024) [paper] [code][ask deepwiki]
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[Cell2Sentence]
[Foundation Model]Cell2Sentence: Teaching Large Language Models the Language of Biology (ICML 2024 Poster) [paper] [code] -
[LangCell]
[Foundation Model]LangCell: Language-Cell Pre-training for Cell Identity Understanding (ICML 2024 Poster) [paper] [code][ask deepwiki]
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[CellPLM]
[Foundation Model]CellPLM: Pre-training of Cell Language Model beyond Single Cells (ICLR 2024 Poster) [paper] [code][ask deepwiki]
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[Stanford PhD Thesis]
[Virtual Cell]Engineering Cells Using Artificial Intelligence (© by Yusuf Roohani 2024) [paper] [GitHub Homepage] [Arc profile]
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[CIGS] High-Throughput Profiling of Chemical-Induced Gene Expression across 93,644 Perturbations (Nature Methods 2025) [paper] [中文解读] [dataset] [code]
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[Tahoe-100M] Tahoe-100M: A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling (bioRxiv 2025) [paper] [中文解读] [code]
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[X-Atlas/Orion] Genome-Wide Perturb-Seq Datasets via a Scalable Fix-Cryopreserve Platform for Training Dose-Dependent Biological Foundation Models (bioRxiv 2025) [paper] [中文解读] [dataset]
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[scBaseCount] scBaseCount: An AI Agent-Curated, Uniformly Processed, and Continually Expanding Single Cell Data Repository (bioRxiv 2025) [paper] [code-scRecounter] [code-SRAgent]
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[Sci-Plex] Massively Multiplex Chemical Transcriptomics at Single-Cell Resolution (Science 2019) [paper] [dataset] [code]
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[scPerturb] scPerturb: Harmonized Single-Cell Perturbation Data (Nature Methods 2024) [paper] [dataset] [code]
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[CMAP LINCS 2020] [link]
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[Cell Painting Gallery] [link] [dataset overview] [AWS overview] [Bray dataset]
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[CM4AI] Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines (bioRxiv 2024) [paper] [dataset]
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[RxRx from Recursion] [link]
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[scDrugMap] scDrugMap: benchmarking large foundation models for drug response prediction (Nature Communications 2025) [paper]
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[Insight] From Virtual Cell Challenge to Virtual Organs: Navigating the Deep Waters of Medical AI Models (iCell 2025) [paper]
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[Evaluation] Benchmarking and Evaluation of AI Models in Biology: Outcomes and Recommendations from the CZI Virtual Cells Workshop (arXiv 2025) [paper] [中文解读]
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[Challenge] Virtual Cell Challenge: Toward a Turing Test for the Virtual Cell (Cell Commentary 2025) [paper] [homepage] [beginner's guidance]
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[Symposium] AI Proteomics and Virtual Cell (© by Westlake University 2025) [media] [中文解读]
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[Report] Projections at the Frontier: Snapshot 2025 (© by Decoding Bio's Team 2025) [slide] [中文解读]
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[Post] Chan Zuckerberg Initiative's rBio Uses Virtual Cells to Train AI, Bypassing Lab Work (© by Michael Nuñez 2025) [blog]
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[Blog] AI's Next Frontier: Modeling Life Itself (© by Chan Zuckerberg Initiative 2025) [blog] [中文解读]
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[Blog] The State of Research on Virtual Cell Modeling (© by Will Connell 2025) [blog]
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[Blog] What Are Virtual Cells? Learning "Universal Representations" of Life's Fundamental Unit (© by Elliot Hershberg 2025) [blog]
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[中文 Blog] 什么是虚拟细胞：AI 生物学的“登月时刻”和“苦涩教训” (© by 范阳 2025) [blog]
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[Introduction] Virtual Cells (© by Udara Jay 2025) [blog]
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[Arc Institute] Predicting Cellular Responses to Perturbation across Diverse Contexts with STATE [YouTube]
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[Valence Labs] Virtual Cells: Predict, Explain, Discover [YouTube]
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[EPFL] Virtual Cells and Digital Twins: AI in Personalized Medicine [YouTube]
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[SciLifeLab] Emma Lundberg: AI Virtual Cells Could Revolutionize Biological Science [YouTube]
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[Chan Zuckerberg Initiative] AI Virtual Cell Models: How AI is Accelerating Science [YouTube]
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[Chan Zuckerberg Initiative] CZI's Vision for AI-Powered "Virtual Cells" [YouTube]
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[Podcast] Google DeepMind CEO: We Want to Build a Virtual Cell [YouTube]
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[GEARS]
[Perturbation]Predicting transcriptional outcomes of novel multigene perturbations with GEARS (Nature Biotechnology 2023) [paper] [code][ask deepwiki]
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[Geneformer]
[Foundation Model]Transfer Learning Enables Predictions in Network Biology (Nature 2023) [paper] [code][ask deepwiki]
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[CellOT]
[Perturbation]Learning Single-Cell Perturbation Responses Using Neural Optimal Transport (Nature Methods 2023) [paper] [code][ask deepwiki]
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[tGPT]
[Foundation Model]Generative Pretrai 22BF ning from Large-Scale Transcriptomes for Single-Cell Deciphering (iScience 2023) [paper] [code][ask deepwiki]
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[Virtual Cell]Building the Next Generation of Virtual Cells to Understand Cellular Biology (Biophysical Journal 2023) [paper] -
[Research Highlight]
[Virtual Cell]Simulating a Whole Cell (Nature Methods 2022) [paper] -
[Comment] Personalized Medicine: Time for One-Person Trials (Nature Comment 2015) [paper]
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[Theory]
[Virtual Cell]A Whole-Cell Computational Model Predicts Phenotype from Genotype (Cell 2012) [paper] -
[Virtual Cell] The Virtual Cell - A Candidate Co-Ordinator for "Middle-Out" Modelling of Biological Systems (BIB 2009) [paper]
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[VCell 7.7]
[Virtual Cell]Virtual Cell Modelling and Simulation Software Environment (IET Systems Biology 2008) [paper] [software] -
Quantitative Cell Biology with the Virtual Cell (Trends in Cell Biology 2003) [paper]
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[Review] The Virtual Cell: A Software Environment for Computational Cell Biology (Trends in Biotechnology 2001) [paper]
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[Opinion] Whole-Cell Simulation: A Grand Challenge of the 21st Century (Trends in Biotechnology 2001) [paper]
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[Virtual Cell Challenge] Official challenge site for evaluation and community updates [homepage]
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[Arc Virtual Cell Atlas] Large-scale perturbation atlas and codebase from Arc Institute [repo]
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[VCell Software] Long-running software environment for computational cell biology [site]
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[Noetik OCTO-vc] Technical report and demo for virtual cells in tissue [report] [demo]
If you want to suggest a paper, dataset, benchmark, blog, or project, open an Issue or Pull Request. Please follow CONTRIBUTING.md for the submission format and quality bar.