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.
Tag: Perturbation
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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.
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.
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.
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.
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.
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.
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.
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.
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.
Predicting cellular responses to perturbation across diverse contexts with State
Adduri AK et al. · bioRxiv · 2025-06-27
State preprint (bioRxiv June 2025).
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.
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.
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).
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.
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.
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.
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.
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.