Single-Cell Transcriptomics

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6 papers in the last four weeks, with none the four weeks before. 0.1% of all new papers.

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Prototype Guided Post-pretraining for Single-Cell Representation Learning
May 8, 2026Sachini Weerasekara, Natasha Darras, Sagar Kamarthi +2Representation Learning Continued Pretraining
Sachini Weerasekara∗ · Natasha Darras · Sagar Kamarthi +2
Beyond Continuity: Simulation-free Reconstruction of Discrete Branching Dynamics from Single-cell Snapshots
May 1, 2026Junda Ying, Yuxuan Wang, Bowen Yang +2Schrödinger Bridges Single-Cell Transcriptomics
Beijing International Center for Mathematical Research, Peking University · Center for Data Science, Peking University · School of Mathematical Sciences, Peking University +4
Towards Universal Gene Regulatory Network Inference: Unlocking Generalizable Regulatory Knowledge in Single-cell Foundation Models
May 1, 2026Jiaxin Qi, Hang Li, Yan Cui +2Gene Regulatory Network Inference Single-Cell Transcriptomics
Computer Network Information Center, Chinese Academy of Sciences, Beijing, China · Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China · University of Chinese Academy of Sciences, Beijing, China
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
Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability
Apr 7, 2026Yuheng Liang, Lucy Chhuo, Ahmadreza Argha +8Clinical Outcome Prediction Oncology
UNSW BioMedical Machine Learning Lab (BML), School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia · Garvan Institute of Meidical Research, Sydney, NSW, 2000, Australia · School of Biomedical Engineering, UNSW, Sydney, NSW, 2052, Australia +7
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

May 8, 2026, cs.LGJ/K move · Enter open · S save

Prototype Guided Post-pretraining for Single-Cell Representation Learning

Sachini Weerasekara, Natasha Darras, Sagar Kamarthi, Colles Price+1

Sachini Weerasekara∗ · Natasha Darras · Sagar Kamarthi+2

Representation Learning Continued Pretraining Single-Cell Transcriptomics Single-Cell RNA Sequencing

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Single-cell representation learning (SCRL) from gene expression data offers a way to uncover the complex regulatory logic underlying cellular function. Inspired by large language models in natural language modeling, several single-cell pretrained models have recently been proposed that treat genes as tokens and cells as sentences. However, these models are fundamentally limited by the long-tailed nature of cell-type distributions and struggle to generalize under covariate shifts in gene expression data. While fine-tuning is often used to mitigate these issues, we observe that performance remains bounded. To address this challenge, we introduce CellRefine, a post-pretraining method that operates between the pretraining and fine-tuning stages of a single-cell foundation model. CellRefine uses a multi-faceted objective that incorporates marker-gene sets as structural priors to guide post-pretraining and refine the latent embedding manifold of cells. Across multiple computational biology tasks, empirical results show that CellRefine consistently improves downstream performance, yielding gains up to 15%.

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