Single-Cell Perturbation Modeling

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6 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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CellxPert: Inference-Time MCMC Steering of a Multi-Omics Single-Cell Foundation Model for In-Silico Perturbation
Apr 30, 2026Andac Demir, Erik W. Anderson, Jeremy L. Jenkins +1Single-Cell Perturbation Modeling Genomics
Novartis Biomedical Research
Learning biophysical models of gene regulation with probability flow matching
Apr 27, 2026Suryanarayana Maddu, Victor Chardès, Michael J. ShelleyFlow Matching Single-Cell Perturbation Modeling
Center for Computational Biology, Flatiron Institute, New York, NY, USA, 10010 · Molecular & Cellular Biology Department, Harvard University, Cambridge, MA 02138 · Courant Institute of Mathematical Sciences, New York University, New York, NY, USA, 10012
SAVE: A Generalizable Framework for Multi-Condition Single-Cell Generation with Gene Block Attention
Apr 18, 2026Jiahao Li, Jiayi Dong, Peng Ye +3Single-Cell Perturbation Modeling Conditional Generative Modeling
College of Computer Science and Artificial Intelligence, Fudan University · Shanghai Key Laboratory of Intelligent Information Processing, Fudan University
SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction
Mar 18, 2026Shuizhou Chen, Lang Yu, Xueqin Lin +12Representation Learning Single-Cell Perturbation Modeling
1Shanghai Artificial Intelligence Laboratory
Central Dogma Transformer II: An AI Microscope for Understanding Cellular Regulatory Mechanisms
Feb 9, 2026Nobuyuki OtaTransformer Interpretability Single-Cell Perturbation Modeling
Independent Researcher, Burlingame, CA, USA

Apr 30, 2026, q-bio.GNJ/K move · Enter open · S save

CellxPert: Inference-Time MCMC Steering of a Multi-Omics Single-Cell Foundation Model for In-Silico Perturbation

Andac Demir, Erik W. Anderson, Jeremy L. Jenkins, Srayanta Mukherjee

Novartis Biomedical Research

Single-Cell Perturbation Modeling Genomics Single-Cell RNA Sequencing

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In this work, we introduce CellxPert, a scalable multimodal foundation model that unifies single-cell and spatial multi-omics within a common representation space. CellxPert jointly encodes transcriptomic (scRNA-seq), chromatin-accessibility (ATAC-seq), and surface-proteomic (CITE-seq) measurements, while directly incorporating MERFISH and imaging mass-cytometry data as 2D or 3D spatial-visual layers. CellxPert facilitates four key downstream tasks out of the box: (i) cell-type annotation across a broad ontology of 154 largely overlapping identities -- the largest label space addressed to date and a stringent test of fine-grained discrimination, (ii) efficient fine-tuning using Low Rank Adaptation (LoRA), (iii) genome-wide transcriptomic response prediction to in-silico perturbations (ISP), and (iv) seamless multi-omic integration across various assays and platforms. Unlike current single-cell foundation models, which approximate gene perturbations by deleting or reordering tokenized gene expression ranks, CellxPert employs a Metropolis-Hastings sampler whose proposal kernel uses the model's masked conditional distributions to transition to new transcriptomic states conditioned on the perturbed genes. This Markov-chain procedure mitigates out-of-distribution artifacts introduced by abrupt token manipulation and produces trajectories that are biologically interpretable. Evaluations on PBMC68K, Replogle Perturb-seq, Systema, and BMMC benchmarks show that CellxPert surpasses classical and state-of-the-art baselines in cell-type annotation, perturbation response prediction, and multi-omic integration.

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Latest papers 35

CellxPert: Inference-Time MCMC Steering of a Multi-Omics Single-Cell Foundation Model for In-Silico Perturbation

Learning biophysical models of gene regulation with probability flow matching

SAVE: A Generalizable Framework for Multi-Condition Single-Cell Generation with Gene Block Attention

SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction

Central Dogma Transformer II: An AI Microscope for Understanding Cellular Regulatory Mechanisms